Paper
12 October 2020 The correlation filter tracking algorithm using multi-feature background perception
Author Affiliations +
Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740P (2020) https://doi.org/10.1117/12.2577002
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan
Abstract
For visual object tracking in the block motion blur deformation background interference and other issues, put forward in combination with characteris-tics of multiple characteristic scale estimate of background perception related filter tracking algorithm, it through in the object area will be the basis of original algorithm and expand, increase extraction Histogram of Oriented Gradient (HOG) and the characteristics of Color Names (CN) to learn more background information filter, improve object localization accuracy. On this basis, the binary matrix is reasonably constructed to improve the effective response of the filter to the object region on the premise of effectively sup-pressing the background, and the size of the object is estimated by using the training scale filter. Experimental and simulation results show that the pro-posed algorithm can solve the problems such as background interference of occlusive motion blur deformation in tracking. In OTB-100 datasets, the ac-curacy and success rate of proposed algorithm are improved by 1.3% and 1.4% respectively. In the background interference sequence of occluding mo-tion blur deformation of OTB-100 datasets, the accuracy of the proposed al-gorithm is 1.9%, 4.0%, 4.3% and 3.4% higher than that of the Backline-Aware Correlation Filters (BACF) algorithm. The FPS can reach 13.7, this result can show that it has high theoretical value and engineering value.
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Yuantao Chen, Volachith Phonevilay, Linwu Liu, and Jiajun Tao "The correlation filter tracking algorithm using multi-feature background perception", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740P (12 October 2020); https://doi.org/10.1117/12.2577002
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KEYWORDS
Detection and tracking algorithms

Electronic filtering

Image filtering

Optical tracking

Feature extraction

Dimension reduction

Optical filters

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