Patch-based object tracking via Locality-constrained Linear Coding

© 2016 TCCT. In this paper, the Locality-constrained Linear Coding(LLC) algorithm is incorporated into the object tracking framework. Firstly, we extract local patches within a candidate and then utilize the LLC algorithm to encode these patches. Based on these codes, we exploit pyramid max pooling strategy to generate a richer feature histogram. The feature histogram which integrates holistic and part-based features can be more discriminative and representative. Besides, an occlusion handling strategy is utilized to make our tracker more robust. Finally, an efficient graph-based manifold ranking algorithm is exploited to capture the relevance between target templates and candidates. For tracking, target templates are taken as labeled nodes while target candidates are taken as unlabeled nodes, and the goal of tracking is to search for the candidate that is the most relevant to existing labeled nodes by manifold ranking algorithm. Experiments on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to other state-of-the-art baselines

Efficient Version-Space Reduction for Visual Tracking

Discrminative trackers, employ a classification approach to separate the target from its background. To cope with variations of the target shape and appearance, the classifier is updated online with different samples of the target and the background. Sample selection, labeling and updating the classifier is prone to various sources of errors that drift the tracker. We introduce the use of an efficient version space shrinking strategy to reduce the labeling errors and enhance its sampling strategy by measuring the uncertainty of the tracker about the samples. The proposed tracker, utilize an ensemble of classifiers that represents different hypotheses about the target, diversify them using boosting to provide a larger and more consistent coverage of the version-space and tune the classifiers' weights in voting. The proposed system adjusts the model update rate by promoting the co-training of the short-memory ensemble with a long-memory oracle. The proposed tracker outperformed state-of-the-art trackers on different sequences bearing various tracking challenges.Comment: CRV'17 Conferenc

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

Fast Visual Tracking Using Spatial Temporal Background Context Learning

Visual Tracking by now has gained much provenience among researchers in recent years due to its vast variety of applications that occur in daily life. Various applications of visual tracking include counting of cars on a high way, analyzing the crowd intensity in a concert or a football ground or a surveillance camera tracking a single person to track its movements. Various techniques have been proposed and implemented in this research domain where researchers have analyzed various parameters. Still this area has a lot to offer. There are two common approaches that are currently deployed in visual tracking. One is discriminative tracking and the other one is generative tracking. Discriminative tracking requires a pre-trained model that requires the learning of the data and solves the object recognition as a binary classification problem. On the other hand, generative model in tracking makes use of the previous states so that next state can be predicted. In this paper, a novel tacking based on generative tracking method is proposed called as Illumination Inavariant Spatio Temporal Tracker (IISTC). The proposed technique takes into account of the nearby surrounding regions and performs context learning so that the state of the object under consideration and its surrounding regions can be estimated in the next frame. The learning model is deployed both in the spatial domain as well as the temporal domain. Spatial domain part of the tracker takes into consideration the nearby pixels in a frame while the temporal model takes account of the possible change of object location. The proposed tracker was tested on a set of 50 images against other state of the art four trackers. Experimental results reveal that our proposed tracker performs reasonably well as compared with other trackers. The proposed visual tracker is both efficiently with respect to computation power as well as accuracy. The proposed tracker takes only 4 fast Fourier transform computations thus making it reasonably faster. The proposed trackers perform exceptionally well when there is a sudden change in back ground illumination

Taming Wild Faces: Web-Scale, Open-Universe Face Identification in Still and Video Imagery

With the increasing pervasiveness of digital cameras, the Internet, and social networking, there is a growing need to catalog and analyze large collections of photos and videos. In this dissertation, we explore unconstrained still-image and video-based face recognition in real-world scenarios, e.g. social photo sharing and movie trailers, where people of interest are recognized and all others are ignored. In such a scenario, we must obtain high precision in recognizing the known identities, while accurately rejecting those of no interest. Recent advancements in face recognition research has seen Sparse Representation-based Classification (SRC) advance to the forefront of competing methods. However, its drawbacks, slow speed and sensitivity to variations in pose, illumination, and occlusion, have hindered its wide-spread applicability. The contributions of this dissertation are three-fold: 1. For still-image data, we propose a novel Linearly Approximated Sparse Representation-based Classification (LASRC) algorithm that uses linear regression to perform sample selection for l1-minimization, thus harnessing the speed of least-squares and the robustness of SRC. On our large dataset collected from Facebook, LASRC performs equally to standard SRC with a speedup of 100-250x. 2. For video, applying the popular l1-minimization for face recognition on a frame-by-frame basis is prohibitively expensive computationally, so we propose a new algorithm Mean Sequence SRC (MSSRC) that performs video face recognition using a joint optimization leveraging all of the available video data and employing the knowledge that the face track frames belong to the same individual. Employing MSSRC results in a speedup of 5x on average over SRC on a frame-by-frame basis. 3. Finally, we make the observation that MSSRC sometimes assigns inconsistent identities to the same individual in a scene that could be corrected based on their visual similarity. Therefore, we construct a probabilistic affinity graph combining appearance and co-occurrence similarities to model the relationship between face tracks in a video. Using this relationship graph, we employ random walk analysis to propagate strong class predictions among similar face tracks, while dampening weak predictions. Our method results in a performance gain of 15.8% in average precision over using MSSRC alone