Real time viterbi optimization of hidden Markov Models for multi target tracking

Abstract:
In this paper the problem of tracking multiple objects in image sequences is studied. A Hidden Markov Model describing the movements of multiple objects is presented. Previously similar models have been used, but in real time system the standard dynamic programming Viterbi algorithm is typically not used to find the global optimum state sequence, 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 delay. 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 maintaining an upper bound on all state sequences not evaluated. 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.