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| Traffic Classification Statistics under Video Surveillance in Forest Areas |
| ZHU Wen-chao,YANG Jie,HE Chao |
| Southwest Forestry University,College of Mechanics and Transportation,Kunming,Yunnan 650224, China |
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Abstract In view of the problems such as difficulty in extracting vehicle features and inability to classify statistics by the traditional method of statistical traffic flow through surveillance video in forest environment, a method of traffic flow classification statistics based on YOLOv5 combined with DeepSORT was proposed. The method used the objective detection algorithm YOLOv5 as a detector to classify and detect vehicles. In order to improve the vehicle detection effect in the actual scene, the CBAM attention mechanism was incorporated into the algorithm to enhance the feature extraction ability of the detector for vehicles. In addition, the NMS was improved to DIoU-NMS so as to solve the problem of missed detection caused by mutual vehicle occlusion. The objective tracking algorithm DeepSORT was used to track the detected vehicles, and the reidentification network was retrained on the vehicle re-identification dataset in order to reduce the vehicle identity switching phenomenon. Finally, the tracked vehicles were counted by setting virtual lines in the video. The results of the method were verified in the actual scenario. As shown by the experimental results, the overall traffic flow statistics accuracy was improved by 10.1% compared with that before the improvement. Besides, the traffic flow statistics accuracy of cars, trucks, and buses reached 91.8%, 94.6% and 93.8% respectively.
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Received: 24 April 2022
Published: 17 July 2023
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2023, Vol. 44 
