一种基于粒子滤波的自适应运动目标跟踪方法

该文提出了一种基于粒子滤波的自适应运动目标跟踪方法。均值漂移算法是一种最优梯度下降法，通过迭代来搜索目标，从而实现对运动目标的跟踪。而粒子滤波是一种在非线性和非高斯情形下进行跟踪的强有力方法。该文首先对图像的直方图进行改进，提出了一种基于统计直方图分布的目标模型，然后通过这个模型将这两种方法有效地结合起来。根据跟踪的过程，自适应地调整参数，能够较好地处理图像序列中由于光线变化或遮挡所带来的影响。实验证明，该文所提出的方法与均值漂移方法相比，即使在复杂的情形下，也能够准确地对目标进行跟踪。

An Adaptive Particle Filter for Moving Objects Tracking

In this paper, an adaptive particle filter for moving objects tracking is proposed. Mean shift is optimization algorithm based on gradient descended, which tracks moving targets through iterations. Particle filter is a robust method of tracking in non-Gauss and non-linear case. Firstly, a target model based on statistical histogram is proposed, which improves the classical histogram. Then Mean Shift algorithm and particle filter are integrated novelly through the statistical histogram target model. The parameters are modified according to the processing of tracking, so the effects caused by changed light or occlusion can be overcome. Experiments show that the method proposed by this paper can track moving target more powerful than Mean Shift tracked. Otherwise, even in complicated case, this method can still efficiently work.