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

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ID: 39947
2019
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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.
Reference Key
zou2019multivehiclesensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zou, Yi;Zhang, Weiwei;Weng, Wendi;Meng, Zhengyun;
Journal sensors
Year 2019
DOI
E1309
URL
Keywords Keywords not found

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