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Sensors 2019, 19(6), 1309; https://doi.org/10.3390/s19061309

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

College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
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Received: 25 January 2019 / Revised: 6 March 2019 / Accepted: 9 March 2019 / Published: 15 March 2019
(This article belongs to the Special Issue Intelligent Vehicles)
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Abstract

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. View Full-Text
Keywords: tracking-by-detection; multi-vehicle tracking; Siamese network; data association; Markov decision process tracking-by-detection; multi-vehicle tracking; Siamese network; data association; Markov decision process
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Zou, Y.; Zhang, W.; Weng, W.; Meng, Z. Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy. Sensors 2019, 19, 1309.

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