【摘要】 针对被跟踪头部目标特征状态随时间变化而与参考模板不匹配的问题,本文提出一种利用先验知识来指导Mean Shift算法中参考模板更新的策略。该方法根据被跟踪目标不同状态下所呈现出颜色的统计特征信息,采用辅助模板对候选模板中的不同颜色特征进行指导性更新,从而形成一个具有目标先验知识的参考模板,解决了模板更新时机选择的难题。实验结果表明,该算法有效解决了因头部旋转而导致模板不匹配的问题,实现了头部的连续跟踪,取得了很好的跟踪效果,并且提高了跟踪算法的自适应能力。更多还原

【Abstract】 To solve the mismatching between candidate model and reference model caused by state changing of the tracked target, a novel model updated method based on object prior knowledge was proposed in Mean Shift framework. The algorithm was implemented by using an auxiliary model constructed on color statistical knowledge of the tracked object under different states. This auxiliary model instructed the update of different color characters. Therefore, a reference model with tracked object prior information was formed. In such way, the time selection problem of model update was also solved by the proposed method. Experiment results under complex scenes show that the novel algorithm overcomes the mismatching of models caused by head rotation effectively and is able to realize continuous head tracking. In addition, the adaptive ability of Mean Shift algorithm is greatly improved.更多还原