Abstract: Multiple feature fusion based tracking is one of the most active research topic in tracking field, but the tracking accuracy needs improving in complex environment and most of them use single fusion rule. In this paper, a new adaptive fusion strategy was proposed for multi-feature fusion. First, the local background information was introduced to strengthen the description of the target, and then the feature weight was calculated by a variety of criteria in the fusion process. In addition, the framework of mean shift was considered to realize target tracking. An extensive number of comparative experimental results show that the proposed algorithm is more stable and robust than the single fusion rule.

Key words: target tracking, multiple feature fusion, multi-criteria, local background, mean shift

摘要： 针对现有基于多特征融合的跟踪算法在复杂环境下跟踪准确度不高,且大部分采用单一判定方式来实现多特征融合的问题,提出了一种综合判定的自适应多特征融合跟踪方法。首先引入局部背景信息加强对目标的描述,然后在多特征融合过程中利用多种判定准则计算特征权值,最后在均值漂移框架下完成对目标的跟踪。在各种场景下的实验结果比较表明:该融合算法比单种判定融合有更好的稳定性和鲁棒性,有效地提高了复杂环境下跟踪准确性。

关键词: 目标跟踪, 多特征融合, 多准则, 局部背景, 均值漂移