基于核密度多伯努利视频多目标跟踪算法

Multi-target visual tracking algorithm based on kernel-density and multi-Bernoulli filter

针对复杂环境下数目变化、目标紧邻及尺寸变化的视频多目标跟踪问题,在多伯努利滤波框架下,提出一种自适应的变数目视频多目标跟踪算法。算法通过引入核密度背景减除技术,可以有效抑制背景干扰;然后融入连续自适应均值漂移(CAMShift)技术,并提出目标紧邻和尺寸变化处理机制,可以有效提高算法的自适应性;最后引入粒子标记技术,可以有效实现对视频多目标的轨迹跟踪。对彩色视频和红外视频序列图像的测试结果表明,本文提出算法可以有效实现对复杂环境下数目变化的视频多目标自适应跟踪,且具有较好的鲁棒性。

To solve the problem that it is difficult to obtain the accurate estimations of the multiple targets in video with complex environment,we propose an adaptive multi-target tracking algorithm under the framework of multi-Bernoulli filter.First,the kernel density background subtraction technique is introduced in this paper,which can effectively restrain the background interference.Then,continuously adaptive mean shift（CAMShift）method is integrated into the framework of multi-Bernoulli filter,and adaptive mechanisms are proposed to handle problems of closely spaced target tracking and the variation of target size.In addition,particle labeling technique is introduced to identify the path of each target in the video.Experimental results show that the proposed algorithm with strong robustness can effectively achieve the visual multi-target tracking in complex circumstances.