Paper
12 June 2020 Previous observation regularized tracker
Yue-en Hou, Wei-guang Li, Jia-ming Deng, Jing-yuan Zeng, Ke-kun Huang, Xiang-yu Zeng
Author Affiliations +
Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115191S (2020) https://doi.org/10.1117/12.2573058
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan
Abstract
Discriminative correlation filter-based trackers get desired performances, but also suffer from unwanted problems such as boundary effect and overfitting. SRDCF tracker uses a spatial penalty matrix to alleviate the effect of boundary effect. STRCF introduces a spatial temporal regularized term to the objective function, which limits the change of filters of two continuous frames. However, both SRDCF and STRCF cannot address the problem of overfitting when targets’ appearance changes dramatically. Our work is an extension of SRDCF tracker and STRCF tracker. We use the observations of previous frames to constraint the filter, so that it is able to mitigate the overfitting problem. Furthermore, we apply incremental principal component analysis (IPCA) to extract the principal information of the previous frames' observations. The objective function can be efficiently solved using the alternating direction method of multipliers (ADMM). Our tracker and 9 state-of-the-art trackers are tested on the OTB-2015 benchmark. The experimental results demonstrate its satisfactory performance.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yue-en Hou, Wei-guang Li, Jia-ming Deng, Jing-yuan Zeng, Ke-kun Huang, and Xiang-yu Zeng "Previous observation regularized tracker", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115191S (12 June 2020); https://doi.org/10.1117/12.2573058
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KEYWORDS
Image filtering

Detection and tracking algorithms

Electronic filtering

Convolution

Optical tracking

Principal component analysis

Algorithm development

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