【摘要】 针对Mean-Shift算法稳定性差、无法适应目标遮挡的特点,提出了一种融合角点特征与颜色特征的目标跟踪算法。该算法利用Harris角点的特征不变性克服了Mean-Shift算法鲁棒性差的缺点,同时利用Mean-Shift算法中核概率密度估计特性克服了目标与背景角点难以区分的缺点。通过视频序列对该算法的跟踪稳定性与抗遮挡性能进行测试,结果表明,新算法的跟踪稳定性与抗遮挡能力优于基于单一角点或颜色特征的Mean-Shift算法。更多还原

【Abstract】 A novel target tracking algorithm fused with the corner feature and the color feature is proposed to solve the poor stability and the anti-blocking capability of the Mean-Shift algorithm.The invariance of Harris corner is used to solve the weak robustness of the Mean-Shift algorithm,and the kernel probability density estimation of the Mean-Shift algorithm is used to improve the ability of distinguishing target corners from background corners.Using a group of videos to test the proposed algorithm,the results show that the tracking stability and the anti-blocking capability of this algorithm are better than that of the Mean-Shift algorithm with single corner feature or color feature.更多还原