中文摘要：

针对传统粒子滤波算法粒子重采样产生的粒子贫化现象及单一特征目标跟踪算法鲁棒性较差的问题,提出一种基于信息保留的自适应多特征融合目标跟踪算法.该算法的信息保留策略在粒子重采样阶段通过优化粒子权重值分布来适当提高小权重粒子的权重并改进了粒子重采样方法,有效抑制了粒子贫化现象,保留更多粒子信息.根据环境变化对特征有效性的影响及不同特征对目标的贡献度,自适应调节多特征模型中各特征分量的权重.实验结果表明,本文算法能有效应对目标形变、目标部分遮挡、背景相似物体干扰等复杂情况,具有良好的跟踪精度和鲁棒性.

英文摘要：

In order to solve the problem of particle impoverishment phenomenon caused by particle resampling and the poor robustness problem of using single feature in the object tracking process ,an adaptive multi-feature fusion object tracking algorithm based on informarion retention is proposed. The proposed strategy of information retention can effectively alleviate particle impoverishment and the weight values of the particles with small weight values are increased, by optimizing the distribution of particle weight values, more particle information is reserved by improving particle resampling method. Feature effectiveness is affected by changing environment and contribution to tracking object of each features, so the weight of each feature component for multi-feature model is adaptively adjusted. Experimental results show that the proposed algorithm can effectively deal with some challenging scenarios such as object deformation,partial occlusion, and interference of similar objects, which has high tracking accuracy and good robustness.