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Robust object tacking based on self-adaptive search area
Author(s): Taihang Dong; Sheng Zhong
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Paper Abstract

Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in the unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.

Paper Details

Date Published: 19 February 2018
PDF: 12 pages
Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080C (19 February 2018); doi: 10.1117/12.2284999
Show Author Affiliations
Taihang Dong, Huazhong Univ. of Science and Technology (China)
Sheng Zhong, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10608:
MIPPR 2017: Automatic Target Recognition and Navigation
Jianguo Liu; Jayaram K. Udupa; Hanyu Hong, Editor(s)

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