Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algori...Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algorithm,the Kalman filter(KF),which is only suitable for linear problems,is replaced by the extended Kalman filter(EKF),which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient(HOG)of the target.The multi-target tracking framework was constructed with YOLO V5 target detection algorithm.An efficient and longrunning Traffic Flow Statistical framework(TFSF)is established based on the tracking framework.Virtual lines are set up to record the movement direction of vehicles to more accurate and detailed statistics of traffic flow.In order to verify the robustness and accuracy of the traffic flow statistical framework,the traffic flow in different scenes of actual road conditions was collected for verification.The experimental validation shows that the accuracy of the traffic statistics framework reaches more than 93%,and the running speed under the detection data set in this paper is 32.7FPS,which can meet the real-time requirements and has a particular significance for the development of intelligent transportation.展开更多
Accurate tracking and statistics analysis of pedestrian flow have wide applications in public scenarios.However,the conventional tracking-by-detection approaches are prone to missing individuals in densely populated o...Accurate tracking and statistics analysis of pedestrian flow have wide applications in public scenarios.However,the conventional tracking-by-detection approaches are prone to missing individuals in densely populated or poorly lit environments.This study introduces a pedestrian detection and flow statistics method based on data fusion,which effectively tracks pedestrians across varying crowd densities.The proposed method amalgamates object detection strategies with crowd counting technique to determine the locations of all pedestrians.By observing the coordinates of pedestrians’foot points,this approach assesses the interaction dynamics between the movement trajectories of pedestrians and designated spatial areas,thereby enabling the collection of flow statistics.Experimental results indicate that the proposed method identifies 2.7 times more pedestrians than object detection methods alone and decreases false positives by 58%compared to crowd counting techniques in crowded settings.In conclusion,the proposed method exhibits considerable promise for achieving accurate pedestrian detection and flow analysis.展开更多
With enormous growth of the number of Internet users and appearance of new applications, characterization of Internet traffic has attracted more and more attention and has become one of the major challenging issues in...With enormous growth of the number of Internet users and appearance of new applications, characterization of Internet traffic has attracted more and more attention and has become one of the major challenging issues in telecommunication network over the past few years. In this paper, we study the network traffic pattern of the aggregate traffic and of specific application traffic, especially the popular applications such as P2P, VoIP that contribute most network traffic. Our study verified that majority Internet backbone traffic is contributed by a small portion of users and a power function can be used to approximate the contribution of each user to the overall traffic. We show that P2P applications are the dominant traffic contributor in current Internet Backbone of China. In addition, we selectively present the traffic pattern of different applications in detail.展开更多
基金This work is supported by the Qingdao People’s Livelihood Science and Technology Plan(Grant 19-6-1-88-nsh).
文摘Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algorithm,the Kalman filter(KF),which is only suitable for linear problems,is replaced by the extended Kalman filter(EKF),which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient(HOG)of the target.The multi-target tracking framework was constructed with YOLO V5 target detection algorithm.An efficient and longrunning Traffic Flow Statistical framework(TFSF)is established based on the tracking framework.Virtual lines are set up to record the movement direction of vehicles to more accurate and detailed statistics of traffic flow.In order to verify the robustness and accuracy of the traffic flow statistical framework,the traffic flow in different scenes of actual road conditions was collected for verification.The experimental validation shows that the accuracy of the traffic statistics framework reaches more than 93%,and the running speed under the detection data set in this paper is 32.7FPS,which can meet the real-time requirements and has a particular significance for the development of intelligent transportation.
基金National Natural Science Foundation of China(No.72174102,No.72334003)Major Consulting Project of Chinese Academy of Engineering(No.2024-XBZD-21).
文摘Accurate tracking and statistics analysis of pedestrian flow have wide applications in public scenarios.However,the conventional tracking-by-detection approaches are prone to missing individuals in densely populated or poorly lit environments.This study introduces a pedestrian detection and flow statistics method based on data fusion,which effectively tracks pedestrians across varying crowd densities.The proposed method amalgamates object detection strategies with crowd counting technique to determine the locations of all pedestrians.By observing the coordinates of pedestrians’foot points,this approach assesses the interaction dynamics between the movement trajectories of pedestrians and designated spatial areas,thereby enabling the collection of flow statistics.Experimental results indicate that the proposed method identifies 2.7 times more pedestrians than object detection methods alone and decreases false positives by 58%compared to crowd counting techniques in crowded settings.In conclusion,the proposed method exhibits considerable promise for achieving accurate pedestrian detection and flow analysis.
文摘With enormous growth of the number of Internet users and appearance of new applications, characterization of Internet traffic has attracted more and more attention and has become one of the major challenging issues in telecommunication network over the past few years. In this paper, we study the network traffic pattern of the aggregate traffic and of specific application traffic, especially the popular applications such as P2P, VoIP that contribute most network traffic. Our study verified that majority Internet backbone traffic is contributed by a small portion of users and a power function can be used to approximate the contribution of each user to the overall traffic. We show that P2P applications are the dominant traffic contributor in current Internet Backbone of China. In addition, we selectively present the traffic pattern of different applications in detail.
