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Vehicle tracking is an important part in intelligent transportation surveillance. But now vehicle tracking faces with the problems such as scale change, the interference of similar color, low resolution video data and so on. In this paper an improved Markov chain Monte Carlo(MCMC) named optical flow MCMC(OF-MCMC) sampling tracking algorithm is proposed for vehicle tracking. First, we use the optical...
Polygonal approximation (PA) of the digital planar curves is an important topic in computer vision community. In this paper, we address this problem in the energy-minimization framework. We present a novel stochastic search scheme, which combines a split-and-merge process and a stochastic approximation Monte Carlo (SAMC) sampling procedure for global optimization. The SAMC sampling method can effectively...
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (SAMC) based tracking scheme for abrupt motion problem in Bayesian filtering framework. In our tracking scheme, the particle weight is dynamically estimated by learning the density of states in simulations, and thus the local-trap...
Tracking of human sperm cells is a challenging task in computer vision due to the motion uncertainty. In this paper, we propose an efficient and effective algorithm for sperm cells tracking which attempts to capture the motion uncertainty of the target object. The tracking problem is formulated within the Bayesian filter framework. To address this problem, we incorporate an orientation adaptive mean...
In this paper, we propose a new algorithm with an adaptive arbitrary support-pixel set, with an arbitrary shape and size, and adaptive support-weight according to perceptual grouping principle in the Markov random field framework. Adaptive arbitrary support-pixel set is a set of pixels which are selected based on the similarity law of the perceptual grouping principle from neighboring pixels of the...
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