Robust object tacking based on self-adaptive search area

Taihang Dong; Sheng Zhong

doi:10.1117/12.2284999

19 February 2018 Robust object tacking based on self-adaptive search area

Taihang Dong, Sheng Zhong

Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 106080C (2018) https://doi.org/10.1117/12.2284999
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

Citation Download Citation

Taihang Dong and Sheng Zhong "Robust object tacking based on self-adaptive search area", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080C (19 February 2018); https://doi.org/10.1117/12.2284999

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KEYWORDS

Detection and tracking algorithms

Motion estimation

Video

Optical tracking

Video surveillance

Optimal filtering

Visualization

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