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Proceedings Paper

Distributed multi-dimensional hidden Markov model: theory and application in multiple-object trajectory classification and recognition
Author(s): Xiang Ma; Dan Schonfeld; Ashfaq Khokhar
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Paper 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.

Paper Details

Date Published: 28 January 2008
PDF: 12 pages
Proc. SPIE 6820, Multimedia Content Access: Algorithms and Systems II, 68200O (28 January 2008); doi: 10.1117/12.766004
Show Author Affiliations
Xiang Ma, Univ. of Illinois at Chicago (United States)
Dan Schonfeld, Univ. of Illinois at Chicago (United States)
Ashfaq Khokhar, Univ. of Illinois at Chicago (United States)

Published in SPIE Proceedings Vol. 6820:
Multimedia Content Access: Algorithms and Systems II
Theo Gevers; Ramesh C. Jain; Simone Santini, Editor(s)

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