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Context-Based Occlusion Detection for Robust Visual Tracking

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Citation Author(s):
Xiaoguang Niu
Yu Qiao
Submitted by:
Xiaoguang Niu
Last updated:
14 September 2017 - 4:13am
Document Type:
Poster
Document Year:
2017
Event:
ICIP 2017
Presenters:
Xiaoguang Niu
Paper Code:
3111
Categories:
Image/Video Processing
 

Occlusion is one of the most challenging factors in visual tracking. In this paper, we propose a novel context-based occlusion detection algorithm for robust visual tracking. The basic idea of our algorithm is that occlusion indicates that
some background points in previous frame move into the target region in current frame. Our algorithm investigates background patches with background trackers. The occlusion is examined by the a occlusion detector. The template updating strategy is that if occlusion is detected, the target template stops updating. Comprehensive experiments in CVPR2013 Online Objecting Tracking Benchmark (OOTB) show that our
tracker achieves comparable performance with other state-ofart trackers.

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