TY  - EJOU
AU  - Mu, Liang 
AU  - Zhao, Hong 
AU  - Li, Yan 
AU  - Liu, Xiaotong 
AU  - Qiu, Junzheng 
AU  - Sun, Chuanlong 

TI  - Traffic Flow Statistics Method Based on Deep Learning and Multi-Feature Fusion
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2021
VL  - 129
IS  - 2
SN  - 1526-1506

AB  - 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 long-running 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.
KW  - Deep learning; multi-target tracking; kalman filter; histogram of oriented gradient; traffic flow statistics

DO  - 10.32604/cmes.2021.017276