AB - © 2015 ACM. 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. AU - Awwad, S AU - Hussein, F AU - Piccardi, M DA - 2015/10/13 DO - 10.1145/2733373.2806295 EP - 1118 JO - MM 2015 - Proceedings of the 2015 ACM Multimedia Conference PY - 2015/10/13 SP - 1115 TI - Local depth patterns for tracking in depth videos Y1 - 2015/10/13 Y2 - 2024/03/29 ER -