[Show abstract][Hide abstract] ABSTRACT:
We present a stochastic tracking algorithm for surveillance videos where targets are dim and of low resolution. Our tracker utilizes the particle filter as the basic framework. Two important novel features of the tracker include: A dynamic motion model consisting of both background and foreground motion parameters is used; Appearance and motion cues are adaptively integrated in a system observation model when estimating the likelihood functions. Based on these features, the accuracy and robustness of the tracker, two important metrics in surveillance applications has been improved. We present the results of applying the proposed algorithm to many sequences with different visual conditions; the algorithm always gives satisfactory results even in some challenging sequences.
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