采用改进辅助粒子滤波的红外多目标跟踪

Multiple infrared target tracking using improved auxiliary particle filter

结合改进的辅助粒子滤波与马尔科夫随机场,提出一种多目标跟踪算法来跟踪红外场景中的多个目标。依据目标区域的灰度直方图描述目标,使用标准辅助粒子滤波对各目标的采样粒子集进行粗略优化,同时在辅助粒子采样过程中引入Mean-shift算法来提高粒子采样效率,解决多目标跟踪时粒子数量呈指数级增长的问题,并进一步提高算法的实时性。针对多目标跟踪常出现的目标遮挡导致跟踪失败的问题,引入图模型理论,利用马尔科夫随机场来表示多目标跟踪模型,将多目标的跟踪问题转换为图模型的推理问题。实验结果表明,该跟踪算法使用较少粒子便能实现跟踪,跟踪正确率达84%,且能有效解决多目标跟踪时的相互遮挡问题。

An algorithm combining an improved Auxiliary Particle Filter（APF） with a Markov random field is proposed to achieve multiple target tracking in an infrared scene.First,targets are described according to the gray histogram of target regions.Then,sampling particles of all targets are optimized roughly by using standard APF.Meanwhile,Mean-shift is introduced to the process of auxiliary particle sampling to improve the exponential growth of particle numbers and to increase the percentage of efficient particles and the real-time ability.As for the failure tracking from that targets often are covered each other,a graphic model theory is introduced,in which multi-tracking model by the Markov random field is used to describe the multi-tracking model and convert the problem of multi-target tracking into an inferential problem of the graph model.Results indicate that the new algorithm proposed can track targets only by a few particles,and the accurate rate for multi-target tracking is up to 84%,the failure tracking caused by targets covered mutually can be solved effectively.