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Automatically tracking human body parts is a difficult problem because of background clutters, missing body parts, and the high degrees of freedoms and complex kinematics of the articulated human body. This paper presents the sequential Markov random fields (SMRFs) for tracking and labeling moving human body parts automatically by learning the spatio-temporal structures of human motions in the setting...
In this paper we propose a technique to detect anomalies in individual and interactive event sequences. We categorize anomalies into two classes: abnormal event, and abnormal context, and model them in the Sequential Monte Carlo framework which is extended by Markov Random Field for tracking interactive events. Firstly, we propose a novel pixel-wise event representation method to construct feature...
Probabilistic visual tracking has been an active research area in the computer vision community in the last few years. Recently, the popular approach to analyze human motion is the use of particle filtering and its extension which mainly based on the Bayes' rule. It has been pointed out that an essential structure involved in the particle filter is quite similar to that in the genetic algorithm. In...
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