多技术融合的Mean-Shift目标跟踪算法

在研究经典算法的基础上,提出了一种多技术融合的Mean-Shift目标跟踪算法,有效地解决了经典Mean-Shift跟踪算法存在的缺陷。通过Kalman算法预测估计目标的中心位置,通过分块颜色直方图提取目标区域的空间信息进行,同时采用背景加权和核加权相结合的方式抑制背景像素对目标的干扰。在多个视频数据上的试验结果表明,研究方法有效地克服了经典的Mean-Shift目标跟踪算法对遮挡、背景像素敏感的问题,在复杂环境的背景下对运动目标跟踪更加准确。

A Mean-Shift target tracking algorithm fused multi technology

Based on the study of classic algorithm, a Mean-Shift target tracking algorithm fused multi-technology was proposed, and the defects of the classic Mean-Shift tracking algorithm were solved. The center position of target was estimated by the Kalman algorithm. The space information of the target area was extracted using the block color histogram. The combination approach of the background weighted and nuclear weighted was used to suppress the interference of background pixels on the target. The experiments resulted on several video data showed that the new method fused three kinds of technology effectively overcame the barrier and background pixel sensitive problem, and had more accurate tracking than classic Mean-Shift target tracking algorithm under complex environment.