Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchmark datasets and applications. In this work, we propose a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel depth segmentation based occlusion detection that stops correlation filter updating and depth masking which adaptively adjusts the spatial support for correlation filter. In Princeton RGBD Tracking Benchmark, our DM-DCF is among the state-of-the-art in overall ranking and the winner on multiple categories. Moreover, since it is based on DCF, “DM-DCF” runs an order of magnitude faster than its competitors making it suitable for time constrained applications.
The number of research papers on generic RGBD (color + depth) object tracking is surprisingly limited despite the fact that depth sensors are ubiquitous and the apparent application of the problem in robotics. One of the reasons is the lack of suitable datasets until recent years. In 2013 Song al @cite_4 introduced the Princeton Dataset for RGBD tracking and nine variations of a baseline tracker. They presented two main approaches; depth as an additional cue and point cloud tracking . In the first case, depth is added as an additional dimension to Histogram of Oriented Gradients (HOG) feature space and in the second case tracking is based on 3D point clouds producing also a 3D bounding box. Their best method performs well, but contains heuristic processing stages and its speed (0.26 FPS) makes the algorithm unsuitable for real-time applications.
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Despite significant progress, tracking is still considered to be a very challenging task. Recently, the increasing popularity of depth sensors has made it possible to obtain reliable depth easily. This may be a game changer for tracking, since depth can be used to prevent model drift and handle occlusion. We also observe that current tracking algorithms are mostly evaluated on a very small number of videos collected and annotated by different groups. The lack of a reasonable size and consistently constructed benchmark has prevented a persuasive comparison among different algorithms. In this paper, we construct a unified benchmark dataset of 100 RGBD videos with high diversity, propose different kinds of RGBD tracking algorithms using 2D or 3D model, and present a quantitative comparison of various algorithms with RGB or RGBD input. We aim to lay the foundation for further research in both RGB and RGBD tracking, and our benchmark is available at http: tracking.cs.princeton.edu.