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Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion

[+] Author Affiliations
Jiatian Pi, Yuzhang Gu, Xiaoliu Cheng, Yunlong Zhan, Yingguan Wang

Shanghai Institute of Microsystem and Information Technology, 865 Changning Road, Shanghai 200050, China

Keli Hu

Shaoxing University, Computer Science and Engineering, 508 Huancheng West Road, Shaoxing 312000, China

J. Electron. Imaging. 25(4), 043022 (Aug 08, 2016). doi:10.1117/1.JEI.25.4.043022
History: Received December 18, 2015; Accepted July 14, 2016
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Abstract.  In visual object tracking, occlusions significantly undermine the performance of tracking algorithms. RGB-D cameras, such as Microsoft Kinect or the related PrimeSense camera, are widely available to consumers. Great attention has been focused on exploiting depth information for object tracking in recent years. We propose an algorithm that improves the existing correlation filter-based tracker for scale-adaptive tracking. Moreover, we utilize depth information provided by the Kinect camera to handle various types of occlusions. First, the optimal location of the target is obtained by the conventional kernelized correlation filter tracker. Then, we make use of the discriminative correlation filter for scale estimation as an independent part. At last, to further improve the tracking performance under occlusions, we present a simple yet effective occlusion handling mechanism to detect occlusion and recovery. In this mechanism, cluster analysis and object segmentation by K-means method have been applied to depth data. Numerous experiments on Princeton RGB-D tracking dataset demonstrate that the proposed algorithm outperforms several state-of-the-art trackers by successfully dealing with occlusions.

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© 2016 SPIE and IS&T

Citation

Jiatian Pi ; Yuzhang Gu ; Keli Hu ; Xiaoliu Cheng ; Yunlong Zhan, et al.
"Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion", J. Electron. Imaging. 25(4), 043022 (Aug 08, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.4.043022


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