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Multi-tracker fusion via adaptive outlier detection

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Abstract

In visual tracking task, due to the ubiquitous challenging attributes such as illumination changes, occlusion and target deformation, there hardly exists a tracker that works satisfactorily under various circumstances. To cope with different challenging factors, in this paper, we propose a fusion framework to absorb the strength of different tracking algorithms for robust object tracking. Our approach focuses on the output fusion of different trackers, without knowing their specific details, which makes our framework quite general to incorporate any new tracker. The proposed framework consists of three main steps. First, it measures the pair-wise correlation between different tracker pairs based on their appearance and geometric consistency. Then, we introduce two effective strategies to identify the unreliable trackers by analyzing the computed pair-wise relationships. Through this outlier detection process, our fusion framework adaptively discards the potential failure trackers and weights the rest trackers differently. Finally, the fusion result is derived from weighted combination of the outputs from the reliable group of trackers. Extensive experimental results on the challenging OTB-2013 and OTB-2015 datasets demonstrate the effectiveness of the proposed fusion framework.

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Authors and Affiliations

  1. The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China

    Chao Xie, Ning Wang, Wengang Zhou, Weiping Li & Houqiang Li

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  1. Chao Xie
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  2. Ning Wang
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  3. Wengang Zhou
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  4. Weiping Li
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  5. Houqiang Li
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Correspondence to Wengang Zhou.

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Xie, C., Wang, N., Zhou, W. et al. Multi-tracker fusion via adaptive outlier detection. Multimed Tools Appl 78, 2227–2250 (2019). https://doi.org/10.1007/s11042-018-6278-9

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