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.
Another RGBD method was recently proposed by Meshgi al @cite_12 who tackled the problem with an occlusion-aware particle filter framework employing a probabilistic model. Although their algorithm is among the top performers on Princeton Benchmark, the complexity of their model, the number of parameters to be tuned and the slowness of the algorithm (0.9 FPS) makes it unpractical for many applications.
@cite_12
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Enhanced particle filter tracker by latent occlusion flag to handle full occlusion.Handled persistent and or complex occlusions in RGBD sequences.Developed data-driven occlusion mask to evaluate various parts of observation.Fused multiple feature from color and depth domains to gain occlusion robustness. Although appearance-based trackers have been greatly improved in the last decade, they still struggle with challenges that are not fully resolved. Of these challenges, occlusions, which can be long lasting and of a wide variety, are often ignored or only partly addressed due to the difficulty in their treatments. To address this problem, in this study, we propose an occlusion-aware particle filter framework that employs a probabilistic model with a latent variable representing an occlusion flag. The proposed framework prevents losing the target by prediction of emerging occlusions, updates the target template by shifting relevant information, expands the search area for an occluded target, and grants quick recovery of the target after occlusion. Furthermore, the algorithm employs multiple features from the color and depth domains to achieve robustness against illumination changes and clutter, so that the probabilistic framework accommodates the fusion of those features. This method was applied to the Princeton RGBD Tracking Dataset, and the performance of our method with different sets of features was compared with those of the state-of-the-art trackers. The results revealed that our method outperformed the existing RGB and RGBD trackers by successfully dealing with different types of occlusions.