Frontiers of Information Technology & Electronic Engineering
>> 2017,
Volume 18,
Issue 5
doi:
10.1631/FITEE.1500389
Article
Exploiting a depth contextmodel in visual tracking with correlation filter
. College of Computer, National University of Defense Technology, Changsha 410073, China.. National Key Laboratory of Parallel and Distributed Processing, Changsha 410073, China
Available online: 2017-06-22
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Abstract
Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.
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