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As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partitioning with parallel filters. Traditionally, most tracking algorithms assume that a target operates according to a single dynamic model. However, the single-model assumption can cause the tracker to become unstable, particularly when the target has complex motions and when the camera has abrupt ego-motions. In the new tracking algorithm, a target was assumed to operate according to one dynamic model from a finite set of models. The switching process from one model to another was governed by a jump Markov process. Based on the improved MM particle filter framework, we offer a new design strategy that adopts the state-partitioning technique and a bank of parallel extended Kalman filters to construct a better proposal distribution to achieve further estimation accuracy. We have conducted extensive testing for the proposed tracking algorithm, and key outcomes were given in the results section. It has been demonstrated by the experiments that this approach gave significantly improved estimations, enabling the new particle filter to effectively track human subjects.