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- Title
Alleviate Similar Object in Visual Tracking via Online Learning Interference-Target Spatial Structure.
- Authors
Guokai Shi; Tingfa Xu; Jiqiang Luo; Jie Guo; Zishu Zhao
- 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.
- Subjects
OBJECT tracking (Computer vision); OCCLUDED fronts (Meteorology); FOURIER transforms; STAPLE system; INTERFERENCE (Telecommunication)
- Publication
Sensors (14248220), 2017, Vol 17, Issue 10, p2382
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s17102382