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In this paper, a new theoretical framework based on hidden Markov model (HMM) and independent component analysis (ICA) mixture model is presented for content analysis of video, namely ICAMHMM. Unlike the Gaussian mixture observation model commonly used in conventional HMM applications, the observations in the new ICAMHMM are modeled as a mixture of non-Gaussian components. Each non-Gaussian component is formulated by an ICA mixture, reflecting the independence of different components across video frames. In addition, to construct a compact feature space to represent a video frame, ICA is applied on video frames and the ICA coefficients are used to form a compact 2-D feature subspace that makes the subsequent modeling computationally efficient. The model parameters can be identified using supervised learning by the training sequences. The new re-estimation learning formulae of iterative ICAMHMM parameter estimation are derived based on a maximum likelihood function. Employing the identified model, maximum likelihood algorithms are developed to detect and recognize video events. As a case study, golf video sequences are used to test the effectiveness of the proposed algorithm. Experimental results show that the presented method can effectively detect and recognize the recurrent event patterns in video data. The presented new ICAMHMM is generic and can be applied to sequential data analysis in other applications.