Online RGB-D tracking via detection-learning-segmentation

• Published in: 2016 23rd International Conference on Pattern Recognition (ICPR) pp. 1231 - 1236
• Main Authors: Ning An, Xiao-Guang Zhao, Zeng-Guang Hou
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: Adaptation models; Computational modeling; Histograms; Image color analysis; Solid modeling; Target tracking; Three-dimensional displays
• DOI: 10.1109/ICPR.2016.7899805

In this paper, we address the problem of online RGB-D tracking where the target object undergoes significant appearance changes. To sufficiently exploit the color and depth cues, we propose a novel RGB-D tracking framework (DLS) that simultaneously builds the target 2D appearance model and 3D distribution model. The framework decomposes the tracking task into detection, learning and segmentation. The detection and segmentation components locate the target collaboratively by using the two target models. An adaptive depth histogram is proposed in the segmentation component to efficiently locate the target in depth frames. The learning component estimates the detection and segmentation errors, updates the target models from the most confident frames by identifying two kinds of distractors: potential failure and occlusion. Extensive experimental results on a large-scale benchmark dataset show that the proposed method performs favourably against state-of-the-art RGB-D trackers in terms of efficiency, accuracy, and robustness.