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Statistical and Geometric Modeling of Spatio-Temporal Patterns for Video Understanding
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Spatio-temporal patterns abound in the real world, and understanding them computationally holds the promise of enabling a large class of applications such as video surveillance, biometrics, computer graphics and animation. In this dissertation, we study models and algorithms to describe complex spatio-temporal patterns in videos for a wide range of applications. The spatio-temporal pattern recognition problem involves recognizing an input video as an instance of a known class. For this problem, we show that a first order Gauss-Markov process is an appropriate model to describe the space of primitives. We then show that the space of primitives is not a Euclidean space but a Riemannian manifold. We use the geometric properties of this manifold to define distances and statistics. This then paves the way to model temporal variations of the primitives. We then show applications of these techniques in the problem of activity recognition and pattern discovery from long videos. The pattern discovery problem on the other hand, requires uncovering patterns from large datasets in an unsupervised manner for applications such as automatic indexing and tagging. Most state-of-the-art techniques index videos according to the global content in the scene such as color, texture and brightness. In this dissertation, we discuss the problem of activity based indexing of videos. We examine the various issues involved in such an effort and describe a general framework to address the problem. We then design a cascade of dynamical systems model for clustering videos based on their dynamics. We augment the traditional dynamical systems model in two ways. Firstly, we describe activities as a cascade of dynamical systems. This significantly enhances the expressive power of the model while retaining many of the computational advantages of using dynamical models. Secondly, we also derive methods to incorporate view and rate-invariance into these models so that similar actions are clustered together irrespective of the viewpoint or the rate of execution of the activity. We also derive algorithms to learn the model parameters from a video stream and demonstrate how a given video sequence may be segmented into different clusters where each cluster represents an activity. Finally, we show the broader impact of the algorithms and tools developed in this dissertation for several image-based recognition problems that involve statistical inference over non-Euclidean spaces. We demonstrate how an understanding of the geometry of the underlying space leads to methods that are more accurate than traditional approaches. We present examples in shape analysis, object recognition, video-based face recognition, and age-estimation from facial features to demonstrate these ideas.