According to recent research statistics,approximately 30%of people who experienced falls are over the age of 65.Therefore,it is meaningful research to detect it in time and take appropriate measures when falling behav...According to recent research statistics,approximately 30%of people who experienced falls are over the age of 65.Therefore,it is meaningful research to detect it in time and take appropriate measures when falling behavior occurs.In this paper,a fall detection model based on improved human posture estimation algorithm is proposed.The improved human posture estimation algorithm is implemented on the basis of Openpose.An im-proved strategy based on depthwise separable convolution combined with HDC structure is proposed.The depthwise separable convolution is used to replace the convolution neural network structure,which makes the network lightweight and reduces the redundant layer in the network.At the same time,in order to ensure that the image features are not lost and ensure the accuracy of detecting human joint points,HDC structure is introduced.Experiments show that the improved algorithm with HDC structure has higher accuracy in joint point detection.Then,human posture estimation is applied to fall detection research,and fall event modeling is carried out through fall feature extraction.The designed convolution neural network model is used to classify and distinguish falls.The experimental results show that our method achieves 98.53%,97.71%and 97.20%accuracy on three public fall detection data sets.Compared with the experimental results of other methods on the same data set,the model designed in this paper has a certain improvement in system accuracy.The sensitivity is also improved,which will reduce the error detection probability of the system.In addition,this paper also verifies the real-time performance of the model.Even if researchers are experimenting with low-level hardware,it can ensure a certain detection speed without too much delay.展开更多
The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in ...The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.展开更多
Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving highaccuracy in key point localization,which ...Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving highaccuracy in key point localization,which is crucial for intelligent applications,contradicts the lowdetection accuracy of human posture detection models in practical scenarios.To address this issue,a human pose estimation network called AT-HRNet has been proposed,which combines convolu-tional self-attention and cross-dimensional feature transformation.AT-HRNet captures significantfeature information from various regions in an adaptive manner,aggregating them through convolu-tional operations within the local receptive domain.The residual structures TripNeck and Trip-Block of the high-resolution network are designed to further refine the key point locations,wherethe attention weight is adjusted by a cross-dimensional interaction to obtain more features.To vali-date the effectiveness of this network,AT-HRNet was evaluated using the COCO2017 dataset.Theresults show that AT-HRNet outperforms HRNet by improving 3.2%in mAP,4.0%in AP75,and3.9%in AP^(M).This suggests that AT-HRNet can offer more beneficial solutions for human posture estimation.展开更多
When associating data from various sensors to estimate the posture of mobile robots, a crucial problem to be solved is that there may be some delayed measurements. Furthermore, the general multi-sensor data fusion alg...When associating data from various sensors to estimate the posture of mobile robots, a crucial problem to be solved is that there may be some delayed measurements. Furthermore, the general multi-sensor data fusion algorithm is a Kalman filter. In order to handle the problem concerning delayed measurements, this paper investigates a Kalman filter modified to account for the delays. Based on the interpolating measurement, a fusion system is applied to estimate the posture of a mobile robot which fuses the data from the encoder and laser global position system using the extended Kalman filter algorithm. Finally, the posture estimation experiment of the mobile robot is given whose result verifies the feasibility and efficiency of the algorithm.展开更多
Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assess-ment(REBA)pose estimation by leveraging progress in three-dimensional human modeling.This survey provides an extensive o...Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assess-ment(REBA)pose estimation by leveraging progress in three-dimensional human modeling.This survey provides an extensive overview of recent advancements,particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks.By reviewing literature from 2016 to 2024,this study offers a current and comprehensive analysis of techniques,existing challenges,and emerging trends in three-dimensional human pose estimation.In contrast to traditional reviews organized by learning paradigms,this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder(MSD)assessments,focusing on essential advancements,comparative analyses,and ergonomic implications.We extend existing image-based clas-sification schemes by examining state-of-the-art two-dimensional models that enhance monocular three-dimensional prediction accuracy and analyze skeleton representations by evaluating joint connectivity and spatial configuration,offering insights into how structural variability influences model robustness.A core contribution of this work is the identification of a critical research gap:the limited exploration of estimating REBA scores directly from single RGB images using monocular three-dimensional pose estimation.Most existing studies depend on depth sensors or sequential inputs,limiting applicability in real-time and resource-constrained environments.Our review emphasizes this gap and proposes future research directions to develop accurate,lightweight,and generalizable models suitable for practical deployment.This survey is a valuable resource for researchers and practitioners in computer vision,ergonomics,and related disciplines,offering a structured understanding of current methodologies and guidance for future innovation in three-dimensional human pose estimation for REBA-based ergonomic risk assessment.展开更多
Vehicle model recognition(VMR)benefits the parking,surveillance,and tolling system by automatically identifying the exact make and model of the passing vehicles.Edge computing technology enables the roadside facilitie...Vehicle model recognition(VMR)benefits the parking,surveillance,and tolling system by automatically identifying the exact make and model of the passing vehicles.Edge computing technology enables the roadside facilities and mobile cameras to achcieve VMR in realtime.Current work generally relies on a specific view of the vehicle or requires huge calculation capability to deploy the end-to-end deep learning network.This paper proposes a lightweight two-stage identification method based on object detection and image retrieval techniques,which empowers us the ability of recognizing the vehicle model from an arbitrary view.The first-stage model estimates the vehicle posture using object detection and similarity matching,which is cost-efficient and suitable to be programmed in the edge computing devices;the second-stage model retrieves the vehicle’s label from the dataset based on gradient boosting decision tree(GBDT)algorithm and VGGNet,which is flexible to the changing dataset.More than 8000 vehicle images are labeled with their components’information,such as headlights,windows,wheels,and logos.The YOLO network is employed to detect and localize the typical components of a vehicle.The vehicle postures are estimated by the spatial relationship between different segmented components.Due to the variety of the perspectives,a 7-dimensional vector is defined to represent the relative posture of the vehicle and screen out the images with a similar photographic perspective.Two algorithms are used to extract the features from each image patch:(1)the scale invariant feature transform(SIFT)combined with the bag-of-features(BoF)and(2)pre-trained deep neural network.The GBDT is applied to evaluate the weight of each component regarding its impact on VMR.The descriptors of each component are then aggregated to retrieve the best matching image from the database.The results showed its advantages in terms of accuracy(89.2%)and efficiency,demonstrating the vast potential of applying this method to large-scale vehicle model recognition.展开更多
With the aim of addressing the visual positioning problem of board-to-board(BTB)jacks during the automatic assembly of flexible printed circuit(FPC)in mobile phones,an FPC-BTB jack detection method based on the optimi...With the aim of addressing the visual positioning problem of board-to-board(BTB)jacks during the automatic assembly of flexible printed circuit(FPC)in mobile phones,an FPC-BTB jack detection method based on the optimized You Only Look Once,version 5(YOLOv5)deep learning algorithm was proposed in this study.An FPC-BTB jack real-time detection and positioning system was developed for the real-time target detection and pose output synchronization of the BTB jack.On that basis,a visual positioning experimental platform that integrated a UR5e manipulator arm and Hikvision industrial camera was built for BTB jack detection and positioning experiments.As indicated by the experimental results,the developed FPC-BTB jack detection and positioning system for BTB target recognition and positioning achieved a success rate of 99.677%.Its average detection accuracy reached 99.341%,the average confidence of the detected target was 91%,the detection and positioning speed reached 31.25 frames per second,and the positioning deviation was less than 0.93 mm,which conforms to the practical application requirements of the FPC assembly process.展开更多
文摘According to recent research statistics,approximately 30%of people who experienced falls are over the age of 65.Therefore,it is meaningful research to detect it in time and take appropriate measures when falling behavior occurs.In this paper,a fall detection model based on improved human posture estimation algorithm is proposed.The improved human posture estimation algorithm is implemented on the basis of Openpose.An im-proved strategy based on depthwise separable convolution combined with HDC structure is proposed.The depthwise separable convolution is used to replace the convolution neural network structure,which makes the network lightweight and reduces the redundant layer in the network.At the same time,in order to ensure that the image features are not lost and ensure the accuracy of detecting human joint points,HDC structure is introduced.Experiments show that the improved algorithm with HDC structure has higher accuracy in joint point detection.Then,human posture estimation is applied to fall detection research,and fall event modeling is carried out through fall feature extraction.The designed convolution neural network model is used to classify and distinguish falls.The experimental results show that our method achieves 98.53%,97.71%and 97.20%accuracy on three public fall detection data sets.Compared with the experimental results of other methods on the same data set,the model designed in this paper has a certain improvement in system accuracy.The sensitivity is also improved,which will reduce the error detection probability of the system.In addition,this paper also verifies the real-time performance of the model.Even if researchers are experimenting with low-level hardware,it can ensure a certain detection speed without too much delay.
基金supported by the Medical Special Cultivation Project of Anhui University of Science and Technology(Grant No.YZ2023H2B013)the Anhui Provincial Key Research and Development Project(Grant No.2022i01020015)the Open Project of Key Laboratory of Conveyance Equipment(East China Jiaotong University),Ministry of Education(KLCE2022-01).
文摘The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.
基金the National Natural Science Foundation of China(No.61975015)the Research and Innovation Project for Graduate Students at Zhongyuan University of Technology(No.YKY2024ZK14).
文摘Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving highaccuracy in key point localization,which is crucial for intelligent applications,contradicts the lowdetection accuracy of human posture detection models in practical scenarios.To address this issue,a human pose estimation network called AT-HRNet has been proposed,which combines convolu-tional self-attention and cross-dimensional feature transformation.AT-HRNet captures significantfeature information from various regions in an adaptive manner,aggregating them through convolu-tional operations within the local receptive domain.The residual structures TripNeck and Trip-Block of the high-resolution network are designed to further refine the key point locations,wherethe attention weight is adjusted by a cross-dimensional interaction to obtain more features.To vali-date the effectiveness of this network,AT-HRNet was evaluated using the COCO2017 dataset.Theresults show that AT-HRNet outperforms HRNet by improving 3.2%in mAP,4.0%in AP75,and3.9%in AP^(M).This suggests that AT-HRNet can offer more beneficial solutions for human posture estimation.
文摘When associating data from various sensors to estimate the posture of mobile robots, a crucial problem to be solved is that there may be some delayed measurements. Furthermore, the general multi-sensor data fusion algorithm is a Kalman filter. In order to handle the problem concerning delayed measurements, this paper investigates a Kalman filter modified to account for the delays. Based on the interpolating measurement, a fusion system is applied to estimate the posture of a mobile robot which fuses the data from the encoder and laser global position system using the extended Kalman filter algorithm. Finally, the posture estimation experiment of the mobile robot is given whose result verifies the feasibility and efficiency of the algorithm.
文摘Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assess-ment(REBA)pose estimation by leveraging progress in three-dimensional human modeling.This survey provides an extensive overview of recent advancements,particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks.By reviewing literature from 2016 to 2024,this study offers a current and comprehensive analysis of techniques,existing challenges,and emerging trends in three-dimensional human pose estimation.In contrast to traditional reviews organized by learning paradigms,this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder(MSD)assessments,focusing on essential advancements,comparative analyses,and ergonomic implications.We extend existing image-based clas-sification schemes by examining state-of-the-art two-dimensional models that enhance monocular three-dimensional prediction accuracy and analyze skeleton representations by evaluating joint connectivity and spatial configuration,offering insights into how structural variability influences model robustness.A core contribution of this work is the identification of a critical research gap:the limited exploration of estimating REBA scores directly from single RGB images using monocular three-dimensional pose estimation.Most existing studies depend on depth sensors or sequential inputs,limiting applicability in real-time and resource-constrained environments.Our review emphasizes this gap and proposes future research directions to develop accurate,lightweight,and generalizable models suitable for practical deployment.This survey is a valuable resource for researchers and practitioners in computer vision,ergonomics,and related disciplines,offering a structured understanding of current methodologies and guidance for future innovation in three-dimensional human pose estimation for REBA-based ergonomic risk assessment.
基金supported by the Scientific Research Project of Shanghai Science and Technology Commission of China(No.21DZ1200601)the National Natural Science Foundation of China(No.NSFC52108411).
文摘Vehicle model recognition(VMR)benefits the parking,surveillance,and tolling system by automatically identifying the exact make and model of the passing vehicles.Edge computing technology enables the roadside facilities and mobile cameras to achcieve VMR in realtime.Current work generally relies on a specific view of the vehicle or requires huge calculation capability to deploy the end-to-end deep learning network.This paper proposes a lightweight two-stage identification method based on object detection and image retrieval techniques,which empowers us the ability of recognizing the vehicle model from an arbitrary view.The first-stage model estimates the vehicle posture using object detection and similarity matching,which is cost-efficient and suitable to be programmed in the edge computing devices;the second-stage model retrieves the vehicle’s label from the dataset based on gradient boosting decision tree(GBDT)algorithm and VGGNet,which is flexible to the changing dataset.More than 8000 vehicle images are labeled with their components’information,such as headlights,windows,wheels,and logos.The YOLO network is employed to detect and localize the typical components of a vehicle.The vehicle postures are estimated by the spatial relationship between different segmented components.Due to the variety of the perspectives,a 7-dimensional vector is defined to represent the relative posture of the vehicle and screen out the images with a similar photographic perspective.Two algorithms are used to extract the features from each image patch:(1)the scale invariant feature transform(SIFT)combined with the bag-of-features(BoF)and(2)pre-trained deep neural network.The GBDT is applied to evaluate the weight of each component regarding its impact on VMR.The descriptors of each component are then aggregated to retrieve the best matching image from the database.The results showed its advantages in terms of accuracy(89.2%)and efficiency,demonstrating the vast potential of applying this method to large-scale vehicle model recognition.
基金supported by the NSFC-Shenzhen Robot Basic Research Center project(U2013204)the National Natural Science Foundation of China(61973196 and 62373225).
文摘With the aim of addressing the visual positioning problem of board-to-board(BTB)jacks during the automatic assembly of flexible printed circuit(FPC)in mobile phones,an FPC-BTB jack detection method based on the optimized You Only Look Once,version 5(YOLOv5)deep learning algorithm was proposed in this study.An FPC-BTB jack real-time detection and positioning system was developed for the real-time target detection and pose output synchronization of the BTB jack.On that basis,a visual positioning experimental platform that integrated a UR5e manipulator arm and Hikvision industrial camera was built for BTB jack detection and positioning experiments.As indicated by the experimental results,the developed FPC-BTB jack detection and positioning system for BTB target recognition and positioning achieved a success rate of 99.677%.Its average detection accuracy reached 99.341%,the average confidence of the detected target was 91%,the detection and positioning speed reached 31.25 frames per second,and the positioning deviation was less than 0.93 mm,which conforms to the practical application requirements of the FPC assembly process.
