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A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets via feature points. First, the Harris corner detector and the Lucas-Kanade tracker are applied in each frame to detect feature points and their associated velocities. Points that are both spatially co-located and exhibit similar motion are grouped into clusters. Due to the non-Gaussian distribution of the points in a cluster and the multi-modality resulting from multiple targets, a special particle filter, the mixture particle filter, is adopted to model the mixture point distribution over time. Each cluster is treated as a mixture component and is modeled by an individual particle filter. The filters in the mixture are instantiated and initialized by applying the EM algorithm, are reclustered by merging overlapping clusters and splitting spatially disjoint clusters, and are terminated when their component weights drop below a threshold. The advantage of using mixture particle filtering is that it is capable of tracking multiple targets simultaneously and also of handling appearing and disappearing targets. We demonstrate the effectiveness of our method on different PETS datasets.