28 January 2008 Distributed multi-dimensional hidden Markov model: theory and application in multiple-object trajectory classification and recognition
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Abstract
In this paper, we propose a novel distributed causal multi-dimensional hidden Markov model (DHMM). The proposed model can represent, for example, multiple motion trajectories of objects and their interaction activities in a scene; it is capable of conveying not only dynamics of each trajectory, but also interactions information between multiple trajectories, which can be critical in many applications. We firstly provide a solution for non-causal, multi-dimensional hidden Markov model (HMM) by distributing the non-causal model into multiple distributed causal HMMs. We approximate the simultaneous solution of multiple HMMs on a sequential processor by an alternate updating scheme. Subsequently we provide three algorithms for the training and classification of our proposed model. A new Expectation-Maximization (EM) algorithm suitable for estimation of the new model is derived, where a novel General Forward-Backward (GFB) algorithm is proposed for recursive estimation of the model parameters. A new conditional independent subset-state sequence structure decomposition of state sequences is proposed for the 2D Viterbi algorithm. The new model can be applied to many other areas such as image segmentation and image classification. Simulation results in classification of multiple interacting trajectories demonstrate the superior performance and higher accuracy rate of our distributed HMM in comparison to previous models.
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Xiang Ma, Xiang Ma, Dan Schonfeld, Dan Schonfeld, Ashfaq Khokhar, Ashfaq Khokhar, } "Distributed multi-dimensional hidden Markov model: theory and application in multiple-object trajectory classification and recognition", Proc. SPIE 6820, Multimedia Content Access: Algorithms and Systems II, 68200O (28 January 2008); doi: 10.1117/12.766004; https://doi.org/10.1117/12.766004
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