Local Depth Patterns for Tracking in Depth Videos

Publisher:
ACM
Publication Type:
Conference Proceeding
Citation:
MM'15: Proceedings of the 2015 ACM Multimedia Conference, 2015, pp. 1115 - 1118 (4)
Issue Date:
2015-10-13
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Conventional video tracking operates over RGB or grey-level data which contain significant clues for the identification of the targets. While this is often desirable in a video surveillance context, use of video tracking in privacy-sensitive environments such as hospitals and care facilities is often perceived as intrusive. Therefore, in this work we present a tracker that provides effective target tracking based solely on depth data. The proposed tracker is an extension of the popular Struck algorithm which leverages a structural SVM framework for tracking. The main contributions of this work are novel depth features based on local depth patterns and a heuristic for effectively handling occlusions. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Stuck tracker and other state-of-the-art trackers using depth and RGB data.
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