提出一种融合相关滤波与关键点匹配的跟踪算法。利用多个基于支持向量机的相关滤波器,分别对目标进行跟踪和验证,同时建立并实时更新一个目标和背景关键点数据库。在验证跟踪失败后,利用关键点匹配的方法对全局关键点进行分类,根据分类结果对目标关键点进行分析,从而得到重检测结果。实验结果表明,在运动模糊、变形、目标遮挡、消失等复杂跟踪场景下,所提算法比现有算法具有更好的准确性和稳健性。

A tracking algorithm is proposed based on correlation filter fusing with keypoint matching. The object is tracked and verified by multiple support-vector-based correlation filters, respectively. Meanwhile, a database containing the keypoints of the target and the background is established and updated in real-time. After the validation of a tracking failure, the global keypoints are classified by utilizing the keypoint matching method and the target keypoints are analyzed according to these classified results. Thus the redetection results are obtained. The experimental results show that the proposed method has a better accuracy and robustness than those by the existing methods in the complex tracking scenes of motion blur, deformation, object occlusion, disappearance and so on.