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
为保障桥区通航安全,提出一种视觉与船舶自动识别系统(Automatic Identification System,AIS)融合的船舶自动监测方法。基于YOLOv5(You Only Look Once version 5)目标检测算法和Canny算法提取船舶图像轮廓信息,构建桥区水域目标距离、...为保障桥区通航安全,提出一种视觉与船舶自动识别系统(Automatic Identification System,AIS)融合的船舶自动监测方法。基于YOLOv5(You Only Look Once version 5)目标检测算法和Canny算法提取船舶图像轮廓信息,构建桥区水域目标距离、方位和高度视觉测量模型与方法,实现船舶三维定位。利用融合视觉与AIS的船舶航行态势数据建立异常行为检测模型,自动识别、监测桥区水域危险船舶。试验结果表明:在单、多船的情况下视觉与AIS数据关联准确率分别达到98.45%、91.29%;能有效监测桥区船舶的运动状态。本研究可为保障船舶和桥梁的安全提供有效方法。展开更多
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.
文摘为保障桥区通航安全,提出一种视觉与船舶自动识别系统(Automatic Identification System,AIS)融合的船舶自动监测方法。基于YOLOv5(You Only Look Once version 5)目标检测算法和Canny算法提取船舶图像轮廓信息,构建桥区水域目标距离、方位和高度视觉测量模型与方法,实现船舶三维定位。利用融合视觉与AIS的船舶航行态势数据建立异常行为检测模型,自动识别、监测桥区水域危险船舶。试验结果表明:在单、多船的情况下视觉与AIS数据关联准确率分别达到98.45%、91.29%;能有效监测桥区船舶的运动状态。本研究可为保障船舶和桥梁的安全提供有效方法。
