Learning Higher-Order Markov Models for Object Tracking in Image Sequences
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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In this paper the problem of tracking multiple objects in im- age sequences is studied. A Hidden Markov Model describ- ing the movements of multiple objects is presented. Previ- ously similar models have been used, but in real time sys- tem the standard dynamic programming Viterbi algorithm is typically not used to find the global optimum state se- quence, as it requires that all past and future observations are available. In this paper we present an extension to the Viterbi algorithm that allows it to operate on infinite time sequences and produce the optimum with only a finite de- lay. This makes it possible to use the Viterbi algorithm in real time applications. Also, to handle the large state spaces of these models another extension is proposed. The global optimum is found by iteratively running an approximative algorithm with higher and higher precision. The algorithm can determine when the global optimum is found by main- taining an upper bound on all state sequences not evalu- ated. For real time performance some approximations are needed and two such approximations are suggested. The theory has been tested on three real data experiments, all with promising results.