Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on ...Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.展开更多
A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were ...A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.展开更多
To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved...To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.展开更多
With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves t...With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.展开更多
As cyber attacks increase in volume and complexity,it becomes more and more difficult for existing analytical tools to detect previously unseen malware.This paper proposes a cooperative framework to leverage the robus...As cyber attacks increase in volume and complexity,it becomes more and more difficult for existing analytical tools to detect previously unseen malware.This paper proposes a cooperative framework to leverage the robustness of big data analytics and the power of ensemble learning techniques to detect the abnormal behavior.In addition to this proposal,we implement a large scale network abnormal traffic behavior detection system performed by the framework.The proposed model detects the abnormal behavior from large scale network traffic data using a combination of a balanced decomposition algorithm and an ensemble SVM.First,the collected dataset is divided into k subsets based on the similarity between patterns using a parallel map reduce k-means algorithm.Then,patterns are randomly selected from each cluster and balanced training sub datasets are formed.Next,the subsets are fed into the mappers to build an SVM model.The construction of the ensemble is achieved in the reduce phase.The proposed structure closely delivers a high accuracy as the number of iterations increases.Experimental results show a promising gain in detection rate and false alarm compared with other existing models.展开更多
Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicl...Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed.First,detecting vehicles based on deep learning is implemented,and Kalman filtering and feature matching are used to track vehicles.Subsequently,the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine,and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene.The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis.The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems.In addition,the implementation and analysis process show the usability,generalization,and effectiveness of the proposed framework.展开更多
Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reli...Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.展开更多
基金supported by y the Applied Research Advancement Project in Engineering University of PAP(WYY202304)Research and Innovation Team Project in Engineering University of PAP(KYTD202306)Funding for postgraduate education and teaching.
文摘Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.
基金National Natural Science Foundation of China(61701029)。
文摘To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.
基金supported by Basic Science Research Program through the NationalResearch Foundation of Korea (NRF)funded by the Ministry of Education (2020R1A6A1A03040583).
文摘With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.
文摘As cyber attacks increase in volume and complexity,it becomes more and more difficult for existing analytical tools to detect previously unseen malware.This paper proposes a cooperative framework to leverage the robustness of big data analytics and the power of ensemble learning techniques to detect the abnormal behavior.In addition to this proposal,we implement a large scale network abnormal traffic behavior detection system performed by the framework.The proposed model detects the abnormal behavior from large scale network traffic data using a combination of a balanced decomposition algorithm and an ensemble SVM.First,the collected dataset is divided into k subsets based on the similarity between patterns using a parallel map reduce k-means algorithm.Then,patterns are randomly selected from each cluster and balanced training sub datasets are formed.Next,the subsets are fed into the mappers to build an SVM model.The construction of the ensemble is achieved in the reduce phase.The proposed structure closely delivers a high accuracy as the number of iterations increases.Experimental results show a promising gain in detection rate and false alarm compared with other existing models.
文摘Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed.First,detecting vehicles based on deep learning is implemented,and Kalman filtering and feature matching are used to track vehicles.Subsequently,the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine,and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene.The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis.The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems.In addition,the implementation and analysis process show the usability,generalization,and effectiveness of the proposed framework.
基金Supported by the National Natural Science Foundation of China(No.61304205,61502240)Natural Science Foundation of Jiangsu Province(BK20141002)+1 种基金Innovation and Entrepreneurship Training Project of College Students(No.201710300051,201710300050)Foundation for Excellent Undergraduate Dissertation(Design) of Naning University of Information Science & Technology
文摘Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.
