一种抗遮挡与重采样的粒子滤波跟踪算法研究

An anti-occlusion and anti-resampling particle filter tracking algorithm

针对经过多次迭代之后粒子滤波因粒子匮乏,对于光照、遮挡与旋转等问题会出现跟踪精度下降,甚至失败等问题,提出了一种似然分布自适应调整ALD方法,根据噪声因子的大小来自适应调整似然分布状态,增加先验和似然的重叠区域,有效提高滤波的稳定性,减少重采样次数;在跟踪精度不高或失败时,用局部三值模式LTP来判定所要跟踪区域,根据有效粒子所占用的面积采用动态的粒子阈值来减少重采样次数,采用模板更新来继续跟踪。实验结果表明,该算法的采样次数更少,在遮挡、旋转等条件下能有效地跟踪目标。

Particle filters can suffer from particle scarce after a number of iterations. Illumination, face mask and rotation problems can also lead to decrease in tracking accuracy of traditional colors, even tracking failure and other issues. To solve these problems abovementioned, we propose an adaptive likelihood distribution （ALD） method, which can adaptively adjust the likelihood distribution state and increase the overlap region of the prior distributions and likelihood distribution according to the size of the noise factor, thus effectively improving the stability of the filter and reducing the number of resampling. As for the low tracking accuracy or failure moment, we use the local ternary patterns （LTP） to identify the area to be tracked, and utilize the effective particle dynamic threshold to reduce particle resampling frequency. The tracking continues by using new determined templates. Experimental results show that the proposed algorithm has a smaller number of samples and can effectively track the target with occlusion, rotation and other conditions in comparison with the traditional algorithm.