一种新型多特征融合粒子滤波视觉跟踪算法

针对单一视觉信息在动态变化环境下描述目标不够充分、跟踪目标不够稳定的缺点,提出了一种基于粒子滤波框架的新型多特征融合的视觉跟踪算法。采用颜色和形状信息来描述运动模型,通过民主合成策略将两种信息融合在一起,使得跟踪算法能根据当前跟踪形势自适应调整两种信息的权重以期达到最佳的最大似然比,实现信息间的优势互补。在设计粒子滤波跟踪算法时,利用自适应信息融合策略构建似然模型,提高了粒子滤波跟踪算法在复杂场景下的稳健性。实验结果表明,多特征融合跟踪算法不仅能准确、高效地跟踪目标,而且对光照、姿态变化引起的目标表观变化具有良好的鲁棒性。

A novel visual tracking algorithm based on multi-cues fusion and particle filter

To overcome the shortcoming of single visual cue in complex enviroments,a novel robust visual tracking algorithm,based on multi-cues fusion under a particle filter framework is proposed.The color cue and shape cue are ulitlized to represent the target and democratic integration is applied to fusion these two cues,according to the likelihood ratio factors,thusfacilating the tracking algorithm on-line adjusting the weight of two cues and utilizing their strong points.During designing particle filter based tracking algorithm,the likelihood model is constructed dependent on adaptive cue fusion mechanism,thus enhancing the robustness of tracking algorithm.The experimental result shows that the approach proposed not only track the moving object accurately and effectively,but has nice robustness to the appearance variation caused by illuminationand pose changes.