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Sensors 2017, 17(10), 2382; doi:10.3390/s17102382

Alleviate Similar Object in Visual Tracking via Online Learning Interference-Target Spatial Structure

1
School of Optoelectronics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China
2
Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 20 September 2017 / Revised: 13 October 2017 / Accepted: 17 October 2017 / Published: 19 October 2017
(This article belongs to the Section Physical Sensors)
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Abstract

Correlation Filter (CF) based trackers have demonstrated superior performance to many complex scenes in smart and autonomous systems, but similar object interference is still a challenge. When the target is occluded by a similar object, they not only have similar appearance feature but also are in same surrounding context. Existing CF tracking models only consider the target’s appearance information and its surrounding context, and have insufficient discrimination to address the problem. We propose an approach that integrates interference-target spatial structure (ITSS) constraints into existing CF model to alleviate similar object interference. Our approach manages a dynamic graph of ITSS online, and jointly learns the target appearance model, similar object appearance model and the spatial structure between them to improve the discrimination between the target and a similar object. Experimental results on large benchmark datasets OTB-2013 and OTB-2015 show that the proposed approach achieves state-of-the-art performance. View Full-Text
Keywords: similar object interference; correlation filter based trackers; online structured learning similar object interference; correlation filter based trackers; online structured learning
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Shi, G.; Xu, T.; Luo, J.; Guo, J.; Zhao, Z. Alleviate Similar Object in Visual Tracking via Online Learning Interference-Target Spatial Structure. Sensors 2017, 17, 2382.

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