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. In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers. In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an l 1 -regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers. In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an � 1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers.