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.
@cite_4
2163309385 2400779783 2776256943 2156222070
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. 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. The complementary nature of color and depth synchronized information acquired by low cost RGBD sensors poses new challenges and design opportunities in several applications and research areas. Here, we focus on background subtraction for moving object detection, which is the building block for many computer vision applications, being the first relevant step for subsequent recognition, classification, and activity analysis tasks. The aim of this paper is to describe a novel benchmarking framework that we set up and made publicly available in order to evaluate and compare scene background modeling methods for moving object detection on RGBD videos. The proposed framework involves the largest RGBD video dataset ever made for this specific purpose. The 33 videos span seven categories, selected to include diverse scene background modeling challenges for moving object detection. Seven evaluation metrics, chosen among the most widely used, are adopted to evaluate the results against a wide set of pixel-wise ground truths. Moreover, we present a preliminary analysis of results, devoted to assess to what extent the various background modeling challenges pose troubles to background subtraction methods exploiting color and depth information. Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinect-style) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. In this paper, we introduce a large-scale, hierarchical multi-view object dataset collected using an RGB-D camera. The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection, demonstrating that combining color and depth information substantially improves quality of results.