基于RJMCMC的视觉多目标跟踪算法

研究了基于MCMC的多目标跟踪算法。针对MCMC迭代过程中抽样置信度低以及不能进行有效迭代的问题,提出一种新的基于RJMCMC的视觉多目标跟踪算法。给定观测量,将跟踪问题建模为状态量的最大后验估计(MAP)、关于MAP的先验与似然的估计。借助匹配阵给出了目标先验建议分布,设计了4种马氏链可逆运动方式;似然度量采用随空间加权的颜色直方图匹配衡量。MCMC抽样过程中的状态由MS迭代产生,而不是随机走生成。基于似然度量导出了MS迭代式。实验结果及定量分析评估结果说明了本算法的有效性。

Multi-object Visual Tracking Based on Reversible Jump Markov Chain Monte Carlo

MCMC-based multi-object visual tracking was investigated here. To improve the confidence of sampling and perform the iteration effectively,a new approach to multi-object visual tracking was proposed based on reversible jump Markov chain Monte Carlo (RJMCMC) sampling. Uiven image observation, the tracking problem was formulated as computing the MAP (maximum a posteriori) estimation .The prior proposal distribution of object was developed with the aid of association match matrix,and four types of reversible and jump moves were designed for Markov chains dy- namics. I}he likelihood distribution measure was presented via position-weighted colour Kist match between reference objects and candidate objects. The state updating was generated from mean-shift(MS) iteration,rather than from random walk in the MCMC sampling. Experimental results and quantitative evaluation demonstrate that the proposed approach is effective for challenge situations.