Title: Scale-adaptive vehicle tracking based on background information

Authors: Wei Sun; Yuzhou Zhao; Xiaorui Zhang; Yang Wu

Addresses: School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing, China ' School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, China ' Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing, China ' School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, China

Abstract: To solve the problem of low accuracy and poor robustness of vehicle tracking in complex traffic scenes, scale-adaptive vehicle tracking based on background information is therefore proposed to this paper. The traditional correlation filter tracking algorithm is less dependent on background information. This easily leads to tracking error. We propose to use the background information of the vehicle and the surrounding as a sample set to establish a position classifier. It transforms the target tracking problem into the classification of the target and the background. This also improves the position accuracy of the tracking target response point when the background is complex. The dimensions of the vehicle change as the relative distance between the vehicle and the camera changes, affecting the tracking reliability. This algorithm crops Histogram of Oriented Gradient (HOG) features of the different-scale vehicle images and establishes a scale classifier. It determines the best scale of the target built on the output response peak of the scale classifier. This improves the adaptability of classifier against vehicle scale change. Extensive experimental results demonstrate that the method improves the accuracy and robustness of vehicle tracking significantly.

Keywords: vehicle tracking; correlation filters; background information; position classification; scale classification.

DOI: 10.1504/IJES.2020.107045

International Journal of Embedded Systems, 2020 Vol.12 No.3, pp.305 - 314

Received: 17 Sep 2018
Accepted: 18 Dec 2018
Published online: 01 May 2020 *

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