Object tracking with mask-constrained spatial context

Yiyou Guo; Hong Huo; Tao Fang

doi:10.1117/1.JEI.27.6.063007

19 November 2018 Object tracking with mask-constrained spatial context

Yiyou Guo, Hong Huo, Tao Fang

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063007 (November 2018). https://doi.org/10.1117/1.JEI.27.6.063007

Abstract

Discriminative correlation filters (DCFs) have shown excellent performance in visual tracking. DCF substitutes the sliding windows sampling strategy in traditional tracking methods with circular shift of the context area. Via projecting the filter learning into the frequency domain, DCF achieves satisfying performance and speed. Appropriate context area size has an influence on the performance of correlation filters. Small context area limits the CF’s ability to handle fast motion and partial occlusion, whereas large context area leads the CF to suffer from boundary effect. To make use of a large area of context and alleviate the accompanying drift risk, we propose a mask-constrained context correlation filter for object tracking. We first analyze the traditional window strategy via Taylor series and design a spatial mask that can be covered by a larger context area. Furthermore, the shape of the mask is adaptive to the target variation. Extensive experimental results in OTB-2015, VOT-2014, and VOT-2016 datasets demonstrate that this mask-constrained operation can improve the CF tracker performance in a large margin.

Citation Download Citation

Yiyou Guo, Hong Huo, and Tao Fang "Object tracking with mask-constrained spatial context," Journal of Electronic Imaging 27(6), 063007 (19 November 2018). https://doi.org/10.1117/1.JEI.27.6.063007

Received: 12 June 2018; Accepted: 26 October 2018; Published: 19 November 2018

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