摘要
目标运动的多样性以及背景环境的复杂性是影响目标跟踪鲁棒性的主要原因.受背景颜色、光照以及姿态尺度变化等因素的影响,目标模板更新精度不高、目标跟踪鲁棒性差.针对此类问题,提出了一种基于特征学习与特征记忆的模板更新机制,通过构建目标模板库,保存丰富的运动目标信息,采用粒子滤波跟踪算法,将候选模板与模板库中的目标信息进行匹配,确定目标状态实现跟踪.实验结果表明,该算法以更丰富的目标信息进行跟踪,比传统目标模板更新策略的粒子滤波算法具有更高的跟踪精度和更强的鲁棒性.
The diversity of object motion and the complexity of background decrease the robustness of object tracking.Similarity of background colors,changes in illumination and object deformation lower the accuracy of the object template and the robustness of object tracking.To deal with this problem,a template update mechanism based on feature-learning and feature-memory was proposed.The algorithm built an object template library by preserving abundant information of the object.By matching the object with the object template library,the state of the object was obtained and the object was thcn tracked by particle filter.Experimental results show that the proposed method has better accuracy and robustness than the particle filter based on traditional object template update strategies.
基金
国家自然科学基金(61005091
61375079)资助
关键词
特征学习
特征记忆
模板库
粒子滤波
目标跟踪
feature-learning
feature-memory
template library
particle filter
object tracking
