Tracking algorithm based on adaptive state transition
To improve the accuracy and real-time performance of particle filter algorithm for tracking vision object, a tracking algorithm based on adaptive state transition is proposed. Firstly, it employs a zero-order adaptive model to obtain the state of the target, and then uses local optimization characteristics of the mean-shift algorithm to find the maximum value of posteriori probability. Particle filer is used to produce more samples at the case of multi-peaks and determine the final goal set position with the new particles. Experimental results show this combined tracker provides comparable accu-racy and reduces computation complexity.
国家科技期刊平台
NSTL
万方数据
维普
