基于粒子滤波和在线学习的目标跟踪

摘要

针对粒子滤波跟踪丢失目标后较难恢复的问题，提出一种基于粒子滤波和在陑学习的目标跟踪方法。使用粒子滤波有效的跟踪结果作为正训练样本不断更新样本库，将随机蕨作为分类器检测目标位置，当分类器和粒子滤波的检测结果存在较大差异时，重新初始化粒子滤波器。在陑学习采用二维二值特征，具有计算简单、尺度不变和光照不变的特点。实验结果证明，该方法的跟踪结果优于传统的粒子滤波，能够准确地跟踪到被遮挡和陭失再出现的目标。%For the problem that the tracker is hard to be resumed when particle filtering fails to track the target, this paper introduces a method that combines particle filtering with online learning. It uses the validated result of particle filtering as positive sample to update the training set. It uses random ferns as classifier to detect object. When there is a big difference between two results, the particle filter will be reinitialized. Two bit binary pattern is used as the online learning feature. It is easy to be computed, and has invariance to illumination and scale. Experimental result proves that this method has better tracking result than particle filtering and it can track the sheltered and disappeared target.