A novel object tracking algorithm based on compressed sensing and entropy of information

Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant no. 20120061110045, (2) the Science and Technology Development Projects of Jilin Province of China under Grant no. 20150204007G X, and (3) the Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China.Peer reviewedPublisher PD

Efficient Asymmetric Co-Tracking using Uncertainty Sampling

Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects. They treat the tracking problem as a classification task and use online learning techniques to update the object model. However, these approaches are heavily invested in the efficiency and effectiveness of their detectors. Evaluating a massive number of samples for each frame (e.g., obtained by a sliding window) forces the detector to trade the accuracy in favor of speed. Furthermore, misclassification of borderline samples in the detector introduce accumulating errors in tracking. In this study, we propose a co-tracking based on the efficient cooperation of two detectors: a rapid adaptive exemplar-based detector and another more sophisticated but slower detector with a long-term memory. The sampling labeling and co-learning of the detectors are conducted by an uncertainty sampling unit, which improves the speed and accuracy of the system. We also introduce a budgeting mechanism which prevents the unbounded growth in the number of examples in the first detector to maintain its rapid response. Experiments demonstrate the efficiency and effectiveness of the proposed tracker against its baselines and its superior performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201

Object Tracking with Multiple Instance Learning and Gaussian Mixture Model

Recently, Multiple Instance Learning (MIL) technique has been introduced for object tracking\linebreak applications, which has shown its good performance to handle drifting problem. While some instances in positive bags not only contain objects, but also contain the background, it is not reliable to simply assume that each feature of instances in positive bags obeys a single Gaussian distribution. In this paper, a tracker based on online multiple instance boosting has been developed, which employs Gaussian Mixture Model (GMM) and single Gaussian distribution respectively to model features of instances in positive and negative bags. The differences between samples and the model are integrated into the process of updating the parameters for GMM. With the Haar-like features extracted from the bags, a set of weak classifiers are trained to construct a strong classifier, which is used to track the object location at a new frame. And the classifier can be updated online frame by frame. Experimental results have shown that our tracker is more stable and efficient when dealing with the illumination, rotation, pose and appearance changes

Understanding and Diagnosing Visual Tracking Systems

Several benchmark datasets for visual tracking research have been proposed in recent years. Despite their usefulness, whether they are sufficient for understanding and diagnosing the strengths and weaknesses of different trackers remains questionable. To address this issue, we propose a framework by breaking a tracker down into five constituent parts, namely, motion model, feature extractor, observation model, model updater, and ensemble post-processor. We then conduct ablative experiments on each component to study how it affects the overall result. Surprisingly, our findings are discrepant with some common beliefs in the visual tracking research community. We find that the feature extractor plays the most important role in a tracker. On the other hand, although the observation model is the focus of many studies, we find that it often brings no significant improvement. Moreover, the motion model and model updater contain many details that could affect the result. Also, the ensemble post-processor can improve the result substantially when the constituent trackers have high diversity. Based on our findings, we put together some very elementary building blocks to give a basic tracker which is competitive in performance to the state-of-the-art trackers. We believe our framework can provide a solid baseline when conducting controlled experiments for visual tracking research

A Study of Exploiting Objectness for Robust Online Object Tracking

Tracking is a fundamental problem in many computer vision applications. Despite the progress over the last decade, there still exist many challenges especially when the problem is posed in real world scenarios (e.g., cluttered background, occluded objects). Among them drifting has been widely observed to be a problem common to the class of online tracking algorithms - i.e., when challenges such as occlusion or nonlinear deformation of the object occurs, the tracker might lose the target completely in subsequent frames in an image sequence. In this work, we propose to exploit the objectness to partially alleviate the drifting problem with the class of online object tracking and verify the effectiveness of this idea by extensive experimental results. More specifically, a recently developed objectness measure was incorporated into Incremental Learning for Visual Tracking (IVT) algorithm in a principled way. We have come up with a strategy of reinitializing the training samples in the proposed approach to improve the robustness of online tracking. Experimental results show that using objectness measure does help to alleviate its drift to background for certain challenging sequences