基于优化权值的多区域采样目标跟踪算法

Multi-region Sampling Object Tracking Algorithm Based on Optimization Weight

针对多区域采样目标跟踪方法容易出现的区域多样性丧失、跟踪精度下降和跟踪不稳定等问题,本文引入区域优化权值及改进子区域重采样方法,提出基于优化权值的多区域采样目标跟踪算法.该方法利用区域优化权值优化各个子区域的区域置信度适当增加低置信度区域在重采样阶段所分配到的粒子数量,在保证粒子根据区域置信度大小有效分配的前提下,抑制了区域多样性丧失现象发生.本文算法在子区域内引入粒子权重优化权值并设定重采样阈值,缓解粒子贫化充分利用有效粒子信息.实验结果表明,本文方法能有效提高目标跟踪精度,改善目标跟踪稳定性.

In this paper, a multi-region sampling object tracking algorithm based on optimization weight is proposed in order to solve the problems of regional diversity and the tracking precision decreased, object tracking instability introduced by object tracking method based on multi-region sampling. In our method,region optimization weight and improved sub-region resampling method is proposed, the regional confidence level of each sub-region is optimized by using the optimization weight to appropriate increase the number of particles low regional confidence level region acquired during resampling phase, under the premise of ensuring the particles is effective allocation according to the regional confidence level, the regional diversity decreasing phenomenon is impactful restrained, In sub-re- gion, the particle weight optimization weight is used to optimize particle weight and set resampling threshold, so as to alleviate particle impoverishment and make full use of effective particle information. Experimental results show that the proposed method can effectively improve the object tracking accuracy and stability.