Vehicle Tracking Algorithm Based on Deep Learning in Roadside Perspective

Guangsheng Han, Qiukun Jin, Hui Rong (), Lisheng Jin and Libin Zhang
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Guangsheng Han: CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd., No.68, Xianfeng East Road, Dongli District, Tianjin 300300, China
Qiukun Jin: School of Vehicle and Energy, Yanshan University, Qinhuangdao 066104, China
Hui Rong: CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd., No.68, Xianfeng East Road, Dongli District, Tianjin 300300, China
Lisheng Jin: School of Vehicle and Energy, Yanshan University, Qinhuangdao 066104, China
Libin Zhang: CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd., No.68, Xianfeng East Road, Dongli District, Tianjin 300300, China

Sustainability, 2023, vol. 15, issue 3, 1-14

Abstract: Traffic intelligence has become an important part of the development of various countries and the automobile industry. Roadside perception is an important part of the intelligent transportation system, which mainly realizes the effective perception of road environment information by using sensors installed on the roadside. Vehicles are the main road targets in most traffic scenes, so tracking a large number of vehicles is an important subject in the field of roadside perception. Considering the characteristics of vehicle-like rigid targets from the roadside view, a vehicle tracking algorithm based on deep learning was proposed. Firstly, we optimized a DLA-34 network and designed a block-N module, then the channel attention and spatial attention modules were added in the front of the network to improve the overall feature extraction ability and computing efficiency of the network. Next, the joint loss function was designed to improve the intra-class and inter-class discrimination ability of the tracking algorithm, which can better discriminate objects of similar appearance and the color of vehicles, alleviate the IDs problem and improve algorithm robustness and the real-time performance of the tracking algorithm. Finally, the experimental results showed that the method had a good tracking effect for the vehicle tracking task from the roadside perspective and could meet the practical application demands of complex traffic scenes.

Keywords: multi-vehicle tracking; roadside perception; roadside view; one-shot; real-time (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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