Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy

Sensors

Weiwei Zhang¹  Yi Zou¹  Wendi Weng¹  Zhengyun Meng¹ 

[1] College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;

关键词: tracking-by-detection;    multi-vehicle tracking;    Siamese network;    data association;    Markov decision process;   

DOI  :  10.3390/s19061309

来源: DOAJ

【 摘 要 】

Online multi-object tracking (MOT) has broad applications in time-critical video analysis scenarios such as advanced driver-assistance systems (ADASs) and autonomous driving. In this paper, the proposed system aims at tracking multiple vehicles in the front view of an onboard monocular camera. The vehicle detection probes are customized to generate high precision detection, which plays a basic role in the following tracking-by-detection method. A novel Siamese network with a spatial pyramid pooling (SPP) layer is applied to calculate pairwise appearance similarity. The motion model captured from the refined bounding box provides the relative movements and aspects. The online-learned policy treats each tracking period as a Markov decision process (MDP) to maintain long-term, robust tracking. The proposed method is validated in a moving vehicle with an onboard NVIDIA Jetson TX2 and returns real-time speeds. Compared with other methods on KITTI and self-collected datasets, our method achieves significant performance in terms of the “Mostly-tracked”, “Fragmentation”, and “ID switch” variables.

【 授权许可】

Unknown