Long-term Tracking with Adaptive Correlation Filters for Object Invisibility
Pages 188 - 193
Abstract
Long-term tracking is one of the most challenging problems in computer vision. During long-term tracking, the target object may suffer from scale changes, illumination changes, heavy occlusions, out-of-view, etc. Most existing tracking methods fail to handle object invisibility, supposing that the object is always visible throughout the image sequence. In this paper, a novel long-term tracking method is proposed, which mainly addresses the problem of object invisibility. We combine a correlation filter based tracker with an online classifier, aiming to estimate the object state and re-detect the object after its invisibility. In addition, an adaptive updating scheme is proposed for the appearance model of the object considering both visible and invisible situations. Quantitative and qualitative evaluations prove that our algorithm outperforms the state-of-the-art methods on the 20 benchmark sequences with object invisibility. Furthermore, the proposed algorithm achieves competitive performance with the state-of-the-art trackers on Object Tracking Benchmark which covers various challenging aspects in object tracking.
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Index Terms
- Long-term Tracking with Adaptive Correlation Filters for Object Invisibility
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Published In
November 2017
237 pages
ISBN:9781450353847
DOI:10.1145/3163080
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Association for Computing Machinery
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Published: 27 November 2017
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- Refereed limited
Funding Sources
- Technology Support Program of Jiangsu Province
- National Natural Science Foundation of China
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ICSPS 2017
ICSPS 2017: The 9th International Conference on Signal Processing Systems
November 27 - 30, 2017
Auckland, New Zealand
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Overall Acceptance Rate 46 of 83 submissions, 55%
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