We propose a novel tracking algorithm that can work robustly in a challenging scenario such that several kinds of appearance and motion changes of an object occur at the same time. Our algorithm is based on a visual tracking decomposition scheme for the efficient design of observation and motion models as well as trackers. In our scheme, the observation model is decomposed into multiple basic observation models that are constructed by sparse principal component analysis (SPCA) of a set of feature templates. Each basic observation model covers a specific appearance of the object. The motion model is also represented by the combination of multiple basic motion models, each of which covers a different type of motion. Then the multiple basic trackers are designed by associating the basic observation models and the basic motion models, so that each specific tracker takes charge of a certain change in the object. All basic trackers are then integrated into one compound tracker through an interactive Markov Chain Monte Carlo (IMCMC) framework in which the basic trackers communicate with one another interactively while run in parallel. By exchanging information with others, each tracker further improves its performance, which results in increasing the whole performance of tracking. Experimental results show that our method tracks the object accurately and reliably in realistic videos where the appearance and motion are drastically changing over time. 1.

We propose a novel algorithm to detect occlusion for visual tracking through learning with observation likelihoods. In our technique, target is divided into regular grid cells and the state of occlusion is determined for each cell using a classifier. Each cell in the target is associated with many small patches, and the patch likelihoods observed during tracking construct a feature vector, which is used for classification. Since the occlusion is learned with patch likelihoods instead of patches themselves, the classifier is universally applicable to any videos or objects for occlusion reasoning. Our occlusion detection algorithm has decent performance in accuracy, which is sufficient to improve tracking performance significantly. The proposed algorithm can be combined with many generic tracking methods, and we adopt L1 minimization tracker to test the performance of our framework. The advantage of our algorithm is supported by quantitative and qualitative evaluation, and successful tracking and occlusion reasoning results are illustrated in many challenging video sequences. 1.

Social interactions are a vital aspect of everyone’s daily living. Individuals with visual impairments are at a loss when it comes to social interactions as majority (nearly 65%) of these interactions happen through visual non-verbal cues. Recently, efforts have been made towards the development of an assistive technology, called the Social Interaction Assistant (SIA)[1], which enables access to non-verbal cues for individuals who are blind or visually impaired. Along with self report feedback about their own social interactions, behavioral psychology studies indicate that individuals with visual impairment will benefit in their social learning and social feedback by gaining access to non-verbal cues of their interaction partners. As part of this larger SIA project, in this paper, we discuss the importance of person localization while building a human-centric assistive technology which addresses the essential needs of the visually impaired users. We describe the challenges that arise when a wearable camera platform is used as a sensor for picking up non-verbal social cues, especially the problem of person localization in a real-world application. Finally, we present a computer vision based algorithm adapted to handle the various challenges associated with the problem of person localization in videos and demonstrate its performance on three examplar video sequences.