基于多特征子空间与核学习的行人再识别

Multi-feature Subspace and Kernel Learning for Person Re-identification

行人再识别指的是在无重叠视域多摄像机监控系统中,匹配不同摄像机视域中的行人目标.针对当前基于距离测度学习的行人再识别算法中存在着特征提取复杂、训练过程复杂和识别效果差的问题,我们提出一种基于多特征子空间与核学习的行人再识别算法.该算法首先在不同特征子空间中基于核学习的方法得到不同特征子空间中的测度矩阵以及相应的相似度函数,然后通过比较不同特征子空间中的相似度之和来对行人进行识别.实验结果表明,本文提出的算法具有较高的识别率,其中在VIPe R数据集上,RANK1达到了40.7%,且对光照变化、行人姿态变化、视角变化和遮挡都具有很好的鲁棒性.

Person re-identification is to match pedestrian images observed from different camera views of non-overlapping multi-camera surveillance systems. The current person re-identification based on metric learning is complicated for feature extraction and training process, and has low performance. Therefore, we propose a multi-feature subspace and kernel learning based method for person re-identification. The distance metric and similarity functions can be achieved firstly in different feature subspaces by kernel learning. Then, the object can be recognized by comparing the sum of similarity of different feature subspaces. Experimental results show that the proposed method has a higher accuracy rate, achieving a40.7 % rank-1 recognition rate on the VIPe R benchmark and that it is robust to different viewpoints, illumination changes,varying poses and the effects of occlusion.