基于角点特征融合的Mean-shift跟踪算法

传统Harris检测算法不能很好地适应跟踪环境。为此，提出一种基于角点特征融合的Mean-shift跟踪算法。考虑人体姿态变化或遮挡对多区域跟踪的影响，采用角点更新策略，将特征融合主色调模型的跟踪结果与多区域跟踪结果进行权衡。实验结果表明，该算法能克服人体姿态变化或遮挡对跟踪的影响，实时性满足一般跟踪系统的要求，且在非遮挡状况下，其跟踪准确率比传统算法高。

Mean-shift Tracking Algorithm Based on Corner Feature Fusion

For traditional Harris detection algorithm cannot suit the situation of tracking well,a algorithm of corner point detection which depends on different ellipse regions is proposed.For the influence of body attitudes variation or occlusion,a strategy of points updating is adopted simultaneously.The result of multi-zone tracking against the global tracking based on dominant hue and multi-features fusion is balanced.Experiments show the algorithm proposed can overcome the body posture change or occlusion effectively,meets the requirement of real-time,and the accuracy of tracking in non-occluded environment is also higher than traditional algorithm.