基于不变特征和核距离度量学习的行人再识别

Invariant Feature and Kernel Distance Metric Learning Based Person Re-Identification

跨摄像机行人因光照、视角、姿态的差异,会使其外观变化显著,给行人再识别的研究带来严峻挑战。文中提出不变特征的核距离度量学习算法进行行人再识别。首先,采用LOMO-FFN不变特征描述子,表示跨摄像机行人的外观;然后,采用KCCA高斯核距离度量学习算法,优化跨摄像机行人特征距离。在具有挑战的VIPeR和PRID450S两个公开数据集上进行仿真实验,实验结果表明所提出的行人再识别算法的先进性和有效性。

Pedestrian may vary greatly in appearance due to differences in illumination, viewpoint, and pose across cameras, which can bring serious challenges in person re-identification. A kernel distance metric learning algorithm of invariant feature is proposed for person re-identification in this paper. Firstly, an invariant feature composed of a concatenation of local maximal occurrence (LOMO) and feature fusion net (FFN) called LOMO-FFN is used to encode human appearance across cameras. Secondly, a gauss kernel distance metric learning algorithm called kernel canonical correlation analysis (KCCA) is applied to obtain an optimized human feature distance across cameras, based on the extracted feature representation. Experimental results have shown that the proposed algorithm effectively improves recognition rates on two challenging datasets (VIPeR, PRID450s).