In today’s information age,video data,as an important carrier of information,is growing explosively in terms of production volume.The quick and accurate extraction of useful information from massive video data has be...In today’s information age,video data,as an important carrier of information,is growing explosively in terms of production volume.The quick and accurate extraction of useful information from massive video data has become a focus of research in the field of computer vision.AI dynamic recognition technology has become one of the key technologies to address this issue due to its powerful data processing capabilities and intelligent recognition functions.Based on this,this paper first elaborates on the development of intelligent video AI dynamic recognition technology,then proposes several optimization strategies for intelligent video AI dynamic recognition technology,and finally analyzes the performance of intelligent video AI dynamic recognition technology for reference.展开更多
Due to the increasing demand for developing a secure and smart living environment, the intelligent video surveillance technology has attracted considerable attention. Building an automatic, reliable, secure, and intel...Due to the increasing demand for developing a secure and smart living environment, the intelligent video surveillance technology has attracted considerable attention. Building an automatic, reliable, secure, and intelligent video surveillance system has spawned large research projects and triggered many popular research topics in several international conferences and workshops recently. This special issue of Journal of ElecWonic Science and Technology （JEST） aims to present recent advances in video surveillance systems which address the observation of people in an environment, leading to a real-time description of their actions and interactions.展开更多
For Unmanned Aerial Vehicles(UAV),the intelligent video analysis is a key technology in intelligent autonomous control,real-time navigation and surveillance.However,poor UAV wireless links would degrade the quality of...For Unmanned Aerial Vehicles(UAV),the intelligent video analysis is a key technology in intelligent autonomous control,real-time navigation and surveillance.However,poor UAV wireless links would degrade the quality of video communication,leading to difficulties in video analysis.To meet the challenges of packet-loss and limited bandwidth in adverse UAV channel environments,this paper proposes a parameter optimization mechanism for UAV intelligent video analysis.In the proposed method,an Optimal Strategy Library(OSL)is designed to optimize the parameters for video encoding and forward error correction.Adapted to the packet-loss rate and bandwidth in practical UAV wireless network,the proposed OSL can facilitate the encoding of video sequences and the recovery of degraded videos with optimal performance.Experimental results demonstrate that the proposed solution can keep intelligent video analysis working efficiently with adverse UAV wireless links,and is capable of maximizing the inference accuracy of Multi-Object Tracking(MOT)algorithms in various scenarios.展开更多
Aiming at the fixed-view video surveillance scene,this paper proposes a video object detection method that combines motion features and YOLO.The method uses the method of filtering video frames without motion features...Aiming at the fixed-view video surveillance scene,this paper proposes a video object detection method that combines motion features and YOLO.The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model.In this process,video frames containing moving objects are first obtained by the moving object detection module.Second,the moving target will be recognized by the object of interest recognition module.Finally,the background decision module records and analyzes the detection results to obtain background model updates or result output.It detects moving objects without using traditional background modeling methods.Experiments based on theCDnet2014 dataset showthat our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62%compared with the YOLO-based humanoid detection method.Furthermore,the method has superior performance in scenarios where target objects appear less frequently(substations,transmission lines,and hazardous areas).展开更多
This study focuses on the multiphase flow properties of crowd motions.Stability is a crucial forewarning factor for the crowd.To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd,a nove...This study focuses on the multiphase flow properties of crowd motions.Stability is a crucial forewarning factor for the crowd.To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd,a novel motion structure analysis model is established based on purposiveness,and is used to describe the continuity of pedestrians’pursuing their own goals.We represent the crowd with self-driven particles using a destination-driven analysis method.These self-driven particles are trackable feature points detected from human bodies.Then we use trajectories to calculate these self-driven particles’purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd.Finally,we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability.Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds,and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors.Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.展开更多
文摘In today’s information age,video data,as an important carrier of information,is growing explosively in terms of production volume.The quick and accurate extraction of useful information from massive video data has become a focus of research in the field of computer vision.AI dynamic recognition technology has become one of the key technologies to address this issue due to its powerful data processing capabilities and intelligent recognition functions.Based on this,this paper first elaborates on the development of intelligent video AI dynamic recognition technology,then proposes several optimization strategies for intelligent video AI dynamic recognition technology,and finally analyzes the performance of intelligent video AI dynamic recognition technology for reference.
文摘Due to the increasing demand for developing a secure and smart living environment, the intelligent video surveillance technology has attracted considerable attention. Building an automatic, reliable, secure, and intelligent video surveillance system has spawned large research projects and triggered many popular research topics in several international conferences and workshops recently. This special issue of Journal of ElecWonic Science and Technology （JEST） aims to present recent advances in video surveillance systems which address the observation of people in an environment, leading to a real-time description of their actions and interactions.
文摘For Unmanned Aerial Vehicles(UAV),the intelligent video analysis is a key technology in intelligent autonomous control,real-time navigation and surveillance.However,poor UAV wireless links would degrade the quality of video communication,leading to difficulties in video analysis.To meet the challenges of packet-loss and limited bandwidth in adverse UAV channel environments,this paper proposes a parameter optimization mechanism for UAV intelligent video analysis.In the proposed method,an Optimal Strategy Library(OSL)is designed to optimize the parameters for video encoding and forward error correction.Adapted to the packet-loss rate and bandwidth in practical UAV wireless network,the proposed OSL can facilitate the encoding of video sequences and the recovery of degraded videos with optimal performance.Experimental results demonstrate that the proposed solution can keep intelligent video analysis working efficiently with adverse UAV wireless links,and is capable of maximizing the inference accuracy of Multi-Object Tracking(MOT)algorithms in various scenarios.
文摘Aiming at the fixed-view video surveillance scene,this paper proposes a video object detection method that combines motion features and YOLO.The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model.In this process,video frames containing moving objects are first obtained by the moving object detection module.Second,the moving target will be recognized by the object of interest recognition module.Finally,the background decision module records and analyzes the detection results to obtain background model updates or result output.It detects moving objects without using traditional background modeling methods.Experiments based on theCDnet2014 dataset showthat our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62%compared with the YOLO-based humanoid detection method.Furthermore,the method has superior performance in scenarios where target objects appear less frequently(substations,transmission lines,and hazardous areas).
基金Project supported by the Shenzhen Science and Technology Innovation Council(No.JCYJ20170410171923840)the National Key R&D Program of China(Nos.2019YFB1310403 and 2019YFB1310402)+2 种基金the National Natural Science Foundation of China(Nos.U1613226 and U1813216)the Chinese University of Hong Kong,Shenzhen(No.PF.01.000143)Shenzhen Institute of Artificial Intelligence and Robotics for Society。
文摘This study focuses on the multiphase flow properties of crowd motions.Stability is a crucial forewarning factor for the crowd.To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd,a novel motion structure analysis model is established based on purposiveness,and is used to describe the continuity of pedestrians’pursuing their own goals.We represent the crowd with self-driven particles using a destination-driven analysis method.These self-driven particles are trackable feature points detected from human bodies.Then we use trajectories to calculate these self-driven particles’purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd.Finally,we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability.Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds,and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors.Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.
