Abstract: A novel infrared moving object tracking method based on particle filter and Mean Shift algorithm was presented. Firstly, it utilized the intensity distribution to represent infrared object, and constructed the observation probability model by statistical histogram. Then, Mean Shift algorithm was incorporated into the propagating process of particle filter, which induced particles distributing within the local area of observation. Compared to the conventional particle filter, the proposed method used much fewer particles to maintain the multi-mode distribution, and overcame the degeneration problem effectively. Experimental results on sequential images show that our method can track steadily when the object moves fast or is occluded, the overall performance of the proposed method is better than traditional particle filter algorithm.Experimental results on sequential images show that our method can track steadily when the object move fast or be occluded, the overall performance of the proposed method is better than traditional particle filter algorithm.

Key words: infrared moving object tracking, particle filter, Mean Shift algorithm, histogram

摘要： 提出一种基于粒子滤波及Mean Shift算法的红外运动目标跟踪方法。该方法首先利用目标区域的灰度分布,建立了一种基于统计直方图的系统观测概率模型,并针对红外目标机动性强，需要大量粒子才能保证算法鲁棒性的问题，将Mean Shift算法引入到粒子更新的过程中，使粒子分布在观测的局部区域内，在利用少量粒子实现分布多样性的同时，有效克服了粒子退化现象。序列图像的实验表明：该算法能够在目标高速运动或发生遮挡的情况下稳健跟踪目标，其总体性能优于传统的粒子滤波算法。

关键词: 红外运动目标跟踪, 粒子滤波, Mean Shift算法, 灰度直方图