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A dynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures, a statistical model for an ensemble of video sequences that is sampled from a finite collection of visual processes, each of which is a dynamic texture. An expectation-maximization (EM) algorithm is derived for learning the parameters of the model, and the model is related to previous works in linear systems, machine learning, time- series clustering, control theory, and computer vision. Through experimentation, it is shown that the mixture of dynamic textures is a suitable representation for both the appearance and dynamics of a variety of visual processes that have traditionally been challenging for computer vision (for example, fire, steam, water, vehicle and pedestrian traffic, and so forth). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (for example, optical flow or other localized motion representations), the mixture of dynamic textures achieves superior performance in the problems of clustering and segmenting video of such processes.
Vehicle dynamics, Computer vision, Video sequences, Machine learning, Clustering algorithms, Machine learning algorithms, Linear systems, Control theory, Fires, Marine vehicles,expectation-maximization, Dynamic texture, temporal textures, video modeling, video clustering, motion segmentation, mixture models, linear dynamical systems, time-series clustering, Kalman filter, probabilistic models
"Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 909-926, May 2008, doi:10.1109/TPAMI.2007.70738
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