For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background mod...For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.展开更多
A new real-time algorithm is proposed in this paperfor detecting moving object in color image sequencestaken from stationary cameras.This algorithm combines a temporal difference with an adaptive background subtractio...A new real-time algorithm is proposed in this paperfor detecting moving object in color image sequencestaken from stationary cameras.This algorithm combines a temporal difference with an adaptive background subtraction where the combination is novel.Ⅷ1en changes OCCUr.the background is automatically adapted to suit the new conditions.Forthe background model,a new model is proposed with each frame decomposed into regions and the model is based not only upon single pixel but also on the characteristic of a region.The hybrid presentationincludes a model for single pixel information and a model for the pixel’s neighboring area information.This new model of background can both improve the accuracy of segmentation due to that spatialinformation is taken into account and salientl5r speed up the processing procedure because porlion of neighboring pixel call be selected into modeling.The algorithm was successfully used in a video surveillance systern and the experiment result showsit call obtain a clearer foreground than the singleframe difference or background subtraction method.展开更多
Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for ima...Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.展开更多
To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction alg...To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.展开更多
Target detection is the fundamental work for perceiving the behavior of cows using video analysis automatically.The videos captured in farming scenes often suffer from a complex background,which leads to difficulty in...Target detection is the fundamental work for perceiving the behavior of cows using video analysis automatically.The videos captured in farming scenes often suffer from a complex background,which leads to difficulty in detecting the target and inconvenience in the subsequent images analysis.In this study,a method was proposed to detect the moving target accurately for cows based on background subtraction.Firstly,the bounding rectangle of cows was calculated using the frames difference method to extract the local background in frames,which were averaged and spliced into one image as the entire background image.Secondly,the size and location of a cow’s body were determined by the bounding rectangle of cows,and the body area was tracked through the video by the binary images.Thirdly,the summation coefficients on RGB channels were adjusted to improve the contrast between the target and background images.Finally,taking the body area in every frame as reference area,the performance of target detection was evaluated by the reference area to determine the optimal summation coefficients on RGB channels,and then background subtraction was processed again to finish the detection.A total of 129 videos were used to test the detection algorithm,and the accuracy of the algorithm was 88.34%,which was 24.85%higher than the classical background subtraction method.The study shows that the algorithm proposed in this study is feasible to detect the target accurately and timely when cows are walking straight in the farming environment under natural light,and this method can improve the detection performance and is an extension to the classical background subtraction method.展开更多
A real-time tracking system for the fast moving object on the complex background is proposed.The Markov random filed（MRF）model based background subtraction algorithm is used to detect the changing pixels and track t...A real-time tracking system for the fast moving object on the complex background is proposed.The Markov random filed（MRF）model based background subtraction algorithm is used to detect the changing pixels and track the moving object.The prior probability of the segmentation mask is modeled by using MRF,and the object tracking task is translated into the maximum a-posterior（MAP）problem.Experimental results show that the method is efficient at both offline and online moving objects on simple and complex background.展开更多
Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-b...Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches,requiring retraining to accommodate diverse animal appearances.This study introduces InteBOMB,an integrated workflow that enhances top-down approaches by incorporating generic object tracking,eliminating the need for prior knowledge of target animals while maintaining broad generalizability.InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings.The“background enhancement”strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps.The“online proofreading”strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features.The“automated labeling suggestion”technique reuses the visual features saved during tracking to identify representative frames for training set labeling.Additionally,the“joint behavior analysis”technique integrates these features with multimodal data,expanding the latent space for behavior classification and clustering.To evaluate the framework,six datasets of mice and six datasets of nonhuman primates were compiled,covering laboratory and natural scenes.Benchmarking results demonstrated a24%improvement in zero-shot generic tracking and a 21%enhancement in joint latent space performance across datasets,highlighting the effectiveness of this approach in robust,generalizable behavior analysis.展开更多
A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for ...A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.展开更多
Objective:Lanqin oral liquid(LOL),as a traditional Chinese medicine prescription,has obvious clinical efficacy in the treatment of pharyngeal inflammation.Exploring the distribution of LOL prototype components and met...Objective:Lanqin oral liquid(LOL),as a traditional Chinese medicine prescription,has obvious clinical efficacy in the treatment of pharyngeal inflammation.Exploring the distribution of LOL prototype components and metabolites in plasma is of great significance for understanding potentially effective compounds.The aim of this study is to elucidate the metabolites and main metabolic pathways of LQL in vivo.Methods:In this study,a reliable approach integrated background subtraction and mass defect filtering(MDF),based on quadrupole time-of-flight mass spectrometry(QTOF-MS)technology,was performed to systematically scan the metabolites of LOL in rat plasma.In addition,according to the prototype mass spectrometry fragmentation pattern and combined with metabolic pathway analysis,a biotransformation oriented analysis strategy was established and applied to the identification of metabolites in LOL in vivo.Results:As a result,159 compounds(58 prototypes and 101 metabolites)were identified or tentatively characterized in drug-containing plasma,including 74 flavonoids,30 alkaloids,34 terpenoids,five phenylpropanoids,six phenolic acids,five fatty acids,and five other type components.The main metabolic pathways include methylation,demethylation,hydroxylation,hydrogenation,glucuronidation,and sulfation.Conclusions:This study provides an overall characterization of the metabolites of LOL in vivo for the first time,providing a solid material basis for exploring the therapeutic effects and pharmacological mechanisms of LOL.展开更多
In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions ...In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.展开更多
Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew back...Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.展开更多
Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods ca...Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.展开更多
For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated back...For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated background and current frame is obtained by using background subtraction, and then an edge-based shadow removal algorithm is implemented. Moreover, a tbresholding segmentation method for the region detection of moving vehicle based on lo- cation search is developed. At the estimation stage, a registration line is set up in the detection area, then the vehicle length is estimated with the horizontal projection technique as soon as the vehicle leaves the registration line. Lastly, the vehicle is classified according to its length and the classification threshold. The proposed method is different from traditional methods that require complex camera calibrations. It calculates the pixel-based vehicle length by using uncalibrated traffic video sequences at lower computational cost. Furthermore, only one registration line is set up, which has high flexibility. Experimental results of three traffic video sequences show that the classification accuracies for the large and small vehicles are 97.1% and 96.7% respectively, which demonstrates the effectiveness of the proposed method.展开更多
Motion segmentation plays an important role in many vision applications,yet it is still a challenging problem in complex scenes.The typical conditions in real world scenarios like illumination variations,dynamic backg...Motion segmentation plays an important role in many vision applications,yet it is still a challenging problem in complex scenes.The typical conditions in real world scenarios like illumination variations,dynamic backgrounds and camera shaking make negative effects on segmentation performance.In this paper,a newly designed method for robust motion segmentation is proposed,which is mainly composed of two interrelated models.One is a normal random model（N-model）,and the other is called enhanced random model（E-model）.They are constructed and updated in spatio-temporal information for adapting to illumination changes and dynamic backgrounds,and operate in an AdaBoost-like strategy.The exhaustive experimental evaluations on complex scenes demonstrate that the proposed method outperforms the state-of-the-art methods.展开更多
TIRF microscopy has provided a means to view mobile granules within 100 nm in size in two dimensions.However quantitative analysis of the position and motion of those granules requires an appropriate tracking method.I...TIRF microscopy has provided a means to view mobile granules within 100 nm in size in two dimensions.However quantitative analysis of the position and motion of those granules requires an appropriate tracking method.In this paper,we present a new tracking algorithm combined with the unique features of TIRF.Firstly a fluorescence correction procedure was processed to solve the problem of fluorescence bleaching over time.Mobile granules were then segmented from a time-lapse image stack by an adaptive background subtraction method.Kalman filter was introduced to estimate and track the granules that allowed reducing searching range and hence greater reliability in tracking process.After the tracked granules were located in x-y plane,the z-position was indirectly inferred from the changes in their intensities.In the experiments the algorithm was applied in tracking GLUT4 vesicles in living adipose cells.The results indicate that the algorithm has achieved robust estimation and tracking of the vesicles in three dimensions.展开更多
The atomic force microscope(AFM)can measure nanoscale morphology and mechanical properties and has a wide range of applications.The traditional method for measuring the mechanical properties of a sample does so for th...The atomic force microscope(AFM)can measure nanoscale morphology and mechanical properties and has a wide range of applications.The traditional method for measuring the mechanical properties of a sample does so for the longitudinal and transverse properties separately,ignoring the coupling between them.In this paper,a data processing and multidimensional mechanical information extraction algorithm for the composite mode of peak force tapping and torsional resonance is proposed.On the basis of a tip–sample interaction model for the AFM,longitudinal peak force data are used to decouple amplitude and phase data of transverse torsional resonance,accurately identify the tip–sample longitudinal contact force in each peak force cycle,and synchronously obtain the corresponding characteristic images of the transverse amplitude and phase.Experimental results show that the measured longitudinal mechanical characteristics are consistent with the transverse amplitude and phase characteristics,which verifies the effectiveness of the method.Thus,a new method is provided for the measurement of multidimensional mechanical characteristics using the AFM.展开更多
An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of m...An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively.展开更多
An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame dif...An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.展开更多
In order to realize automatic and accurate grading of cucumber, the first thing is to make sure the high accuracy and integrity in cucumber shape segmentation. As the core processor of this dissertation, DSP TMS320DM6...In order to realize automatic and accurate grading of cucumber, the first thing is to make sure the high accuracy and integrity in cucumber shape segmentation. As the core processor of this dissertation, DSP TMS320DM6437 acquired and processed digital image, it solved the common shadowing problem associated with the natural light. Ultimately, the background subtraction was proposed. Compared with the result of above-mentioned image data processing, the error rate of classic background subtraction method was often high. The result of optimization showed that the improved background subtraction method worked well, and it could meet an accurate segmentation of the fruit in comparison with the original methods.展开更多
On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detect...On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detection paradigm,a commonly utilized approach,connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the simila-rities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment,a Pearson Simi-larity-centred Kuhn-Munkres(PS-KM)algorithm was proposed in the present study.In this light,the input videos were,initially,gathered from the MOT dataset and converted into frames.The background subtraction occurred whichfiltered the inappropriate data concerning the frames after the frame conversion stage.Then,the extraction of features from the frames was executed.Afterwards,the higher dimensional features were transformed into lower-dimensional features,and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition(IG-SVD).Next,using the Modified Recurrent Neural Network(MRNN)method,classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identi-fied that the recognized objects were tracked.Finally,the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97%accuracy with a low false positive rate(FPR)of 2.3%.It was also proved that the present techniques viz.RNN,CNN,and KNN,were effective with regard to the existing models.展开更多
基金Project(60772080) supported by the National Natural Science Foundation of ChinaProject(3240120) supported by Tianjin Subway Safety System, Honeywell Limited, China
文摘For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.
基金National Natural Science Foundation Grant No.60072029
文摘A new real-time algorithm is proposed in this paperfor detecting moving object in color image sequencestaken from stationary cameras.This algorithm combines a temporal difference with an adaptive background subtraction where the combination is novel.Ⅷ1en changes OCCUr.the background is automatically adapted to suit the new conditions.Forthe background model,a new model is proposed with each frame decomposed into regions and the model is based not only upon single pixel but also on the characteristic of a region.The hybrid presentationincludes a model for single pixel information and a model for the pixel’s neighboring area information.This new model of background can both improve the accuracy of segmentation due to that spatialinformation is taken into account and salientl5r speed up the processing procedure because porlion of neighboring pixel call be selected into modeling.The algorithm was successfully used in a video surveillance systern and the experiment result showsit call obtain a clearer foreground than the singleframe difference or background subtraction method.
基金supported in part by the EU FP7 QUICK project under Grant Agreement No.PIRSES-GA-2013-612652*National Nature Science Foundation of China(No.61671336,61502348,61231015,61671332,U1736206)+3 种基金Hubei Province Technological Innovation Major Project(No.2016AAA015,No.2017AAA123)the Fundamental Research Funds for the Central Universities(413000048)National High Technology Research and Development Program of China(863 Program)No.2015AA016306Applied Basic Research Program of Wuhan City(2016010101010025)
文摘Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.
文摘To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.
基金financially support ed by the general program from the National Natural Science Foundation of China under grant 61473235.
文摘Target detection is the fundamental work for perceiving the behavior of cows using video analysis automatically.The videos captured in farming scenes often suffer from a complex background,which leads to difficulty in detecting the target and inconvenience in the subsequent images analysis.In this study,a method was proposed to detect the moving target accurately for cows based on background subtraction.Firstly,the bounding rectangle of cows was calculated using the frames difference method to extract the local background in frames,which were averaged and spliced into one image as the entire background image.Secondly,the size and location of a cow’s body were determined by the bounding rectangle of cows,and the body area was tracked through the video by the binary images.Thirdly,the summation coefficients on RGB channels were adjusted to improve the contrast between the target and background images.Finally,taking the body area in every frame as reference area,the performance of target detection was evaluated by the reference area to determine the optimal summation coefficients on RGB channels,and then background subtraction was processed again to finish the detection.A total of 129 videos were used to test the detection algorithm,and the accuracy of the algorithm was 88.34%,which was 24.85%higher than the classical background subtraction method.The study shows that the algorithm proposed in this study is feasible to detect the target accurately and timely when cows are walking straight in the farming environment under natural light,and this method can improve the detection performance and is an extension to the classical background subtraction method.
文摘A real-time tracking system for the fast moving object on the complex background is proposed.The Markov random filed（MRF）model based background subtraction algorithm is used to detect the changing pixels and track the moving object.The prior probability of the segmentation mask is modeled by using MRF,and the object tracking task is translated into the maximum a-posterior（MAP）problem.Experimental results show that the method is efficient at both offline and online moving objects on simple and complex background.
基金supported by the STI 2030-Major Projects(2022ZD0211900,2022ZD0211902)STI 2030-Major Projects(2021ZD0204500,2021ZD0204503)+1 种基金National Natural Science Foundation of China(32171461)National Key Research and Development Program of China(2023YFC3208303)。
文摘Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches,requiring retraining to accommodate diverse animal appearances.This study introduces InteBOMB,an integrated workflow that enhances top-down approaches by incorporating generic object tracking,eliminating the need for prior knowledge of target animals while maintaining broad generalizability.InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings.The“background enhancement”strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps.The“online proofreading”strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features.The“automated labeling suggestion”technique reuses the visual features saved during tracking to identify representative frames for training set labeling.Additionally,the“joint behavior analysis”technique integrates these features with multimodal data,expanding the latent space for behavior classification and clustering.To evaluate the framework,six datasets of mice and six datasets of nonhuman primates were compiled,covering laboratory and natural scenes.Benchmarking results demonstrated a24%improvement in zero-shot generic tracking and a 21%enhancement in joint latent space performance across datasets,highlighting the effectiveness of this approach in robust,generalizable behavior analysis.
基金Project supported by National Basic Research Program of Chinaon Urban Traffic Monitoring and Management System(Grant No .TG1998030408)
文摘A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.
文摘Objective:Lanqin oral liquid(LOL),as a traditional Chinese medicine prescription,has obvious clinical efficacy in the treatment of pharyngeal inflammation.Exploring the distribution of LOL prototype components and metabolites in plasma is of great significance for understanding potentially effective compounds.The aim of this study is to elucidate the metabolites and main metabolic pathways of LQL in vivo.Methods:In this study,a reliable approach integrated background subtraction and mass defect filtering(MDF),based on quadrupole time-of-flight mass spectrometry(QTOF-MS)technology,was performed to systematically scan the metabolites of LOL in rat plasma.In addition,according to the prototype mass spectrometry fragmentation pattern and combined with metabolic pathway analysis,a biotransformation oriented analysis strategy was established and applied to the identification of metabolites in LOL in vivo.Results:As a result,159 compounds(58 prototypes and 101 metabolites)were identified or tentatively characterized in drug-containing plasma,including 74 flavonoids,30 alkaloids,34 terpenoids,five phenylpropanoids,six phenolic acids,five fatty acids,and five other type components.The main metabolic pathways include methylation,demethylation,hydroxylation,hydrogenation,glucuronidation,and sulfation.Conclusions:This study provides an overall characterization of the metabolites of LOL in vivo for the first time,providing a solid material basis for exploring the therapeutic effects and pharmacological mechanisms of LOL.
基金The National Natural Science Foundation of China (No.61172135,61101198)the Aeronautical Foundation of China (No.20115152026)
文摘In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.
基金This project was supported by the foundation of the Visual and Auditory Information Processing Laboratory of BeijingUniversity of China (0306) and the National Science Foundation of China (60374031).
文摘Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.
基金supported by the National Key Research and Development Program of China(2017YFA0105203,2017YFA0105201)National Science Foundation of China(31771076,81925011)+2 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(CAS)(XDB32040201)Beijing Academy of Artificial IntelligenceKey-Area Research and Development Program of Guangdong Province(2019B030335001)。
文摘Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.
基金Supported by Key Natural Science Foundation of Hebei Education Department (No.ZD200911)Technology R&D Program of Hebei Province(No.11213518d)
文摘For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated background and current frame is obtained by using background subtraction, and then an edge-based shadow removal algorithm is implemented. Moreover, a tbresholding segmentation method for the region detection of moving vehicle based on lo- cation search is developed. At the estimation stage, a registration line is set up in the detection area, then the vehicle length is estimated with the horizontal projection technique as soon as the vehicle leaves the registration line. Lastly, the vehicle is classified according to its length and the classification threshold. The proposed method is different from traditional methods that require complex camera calibrations. It calculates the pixel-based vehicle length by using uncalibrated traffic video sequences at lower computational cost. Furthermore, only one registration line is set up, which has high flexibility. Experimental results of three traffic video sequences show that the classification accuracies for the large and small vehicles are 97.1% and 96.7% respectively, which demonstrates the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China(61502364)Key Scientific and Technological Project of Henan Province(132102210246)+1 种基金Enterprises-Universities-Research Institutes Cooperation Project of Henan Province(142107000022)CERNET Innovation Project(NGII20150311)
文摘Motion segmentation plays an important role in many vision applications,yet it is still a challenging problem in complex scenes.The typical conditions in real world scenarios like illumination variations,dynamic backgrounds and camera shaking make negative effects on segmentation performance.In this paper,a newly designed method for robust motion segmentation is proposed,which is mainly composed of two interrelated models.One is a normal random model（N-model）,and the other is called enhanced random model（E-model）.They are constructed and updated in spatio-temporal information for adapting to illumination changes and dynamic backgrounds,and operate in an AdaBoost-like strategy.The exhaustive experimental evaluations on complex scenes demonstrate that the proposed method outperforms the state-of-the-art methods.
基金Project supported by the National Natural Science Foundation ofChina (No. 30770596)the Key Laboratory for Biomedical En-gineering of Ministry of Education of China
文摘TIRF microscopy has provided a means to view mobile granules within 100 nm in size in two dimensions.However quantitative analysis of the position and motion of those granules requires an appropriate tracking method.In this paper,we present a new tracking algorithm combined with the unique features of TIRF.Firstly a fluorescence correction procedure was processed to solve the problem of fluorescence bleaching over time.Mobile granules were then segmented from a time-lapse image stack by an adaptive background subtraction method.Kalman filter was introduced to estimate and track the granules that allowed reducing searching range and hence greater reliability in tracking process.After the tracked granules were located in x-y plane,the z-position was indirectly inferred from the changes in their intensities.In the experiments the algorithm was applied in tracking GLUT4 vesicles in living adipose cells.The results indicate that the algorithm has achieved robust estimation and tracking of the vesicles in three dimensions.
基金This project is supported by the General Program of the National Natural Science Foundation of China(62073227)the National Natural Science Foundation of China(61927805 and 61903359).
文摘The atomic force microscope(AFM)can measure nanoscale morphology and mechanical properties and has a wide range of applications.The traditional method for measuring the mechanical properties of a sample does so for the longitudinal and transverse properties separately,ignoring the coupling between them.In this paper,a data processing and multidimensional mechanical information extraction algorithm for the composite mode of peak force tapping and torsional resonance is proposed.On the basis of a tip–sample interaction model for the AFM,longitudinal peak force data are used to decouple amplitude and phase data of transverse torsional resonance,accurately identify the tip–sample longitudinal contact force in each peak force cycle,and synchronously obtain the corresponding characteristic images of the transverse amplitude and phase.Experimental results show that the measured longitudinal mechanical characteristics are consistent with the transverse amplitude and phase characteristics,which verifies the effectiveness of the method.Thus,a new method is provided for the measurement of multidimensional mechanical characteristics using the AFM.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 1/53/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively.
文摘An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.
基金Supported by Heilongjiang Provincial Scientific Research Projects(12521038)China Postdoctoral Science Foundation(20080430886)
文摘In order to realize automatic and accurate grading of cucumber, the first thing is to make sure the high accuracy and integrity in cucumber shape segmentation. As the core processor of this dissertation, DSP TMS320DM6437 acquired and processed digital image, it solved the common shadowing problem associated with the natural light. Ultimately, the background subtraction was proposed. Compared with the result of above-mentioned image data processing, the error rate of classic background subtraction method was often high. The result of optimization showed that the improved background subtraction method worked well, and it could meet an accurate segmentation of the fruit in comparison with the original methods.
文摘On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detection paradigm,a commonly utilized approach,connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the simila-rities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment,a Pearson Simi-larity-centred Kuhn-Munkres(PS-KM)algorithm was proposed in the present study.In this light,the input videos were,initially,gathered from the MOT dataset and converted into frames.The background subtraction occurred whichfiltered the inappropriate data concerning the frames after the frame conversion stage.Then,the extraction of features from the frames was executed.Afterwards,the higher dimensional features were transformed into lower-dimensional features,and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition(IG-SVD).Next,using the Modified Recurrent Neural Network(MRNN)method,classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identi-fied that the recognized objects were tracked.Finally,the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97%accuracy with a low false positive rate(FPR)of 2.3%.It was also proved that the present techniques viz.RNN,CNN,and KNN,were effective with regard to the existing models.
