Due to variations in pose and illumination condition,the appearance of a person can be significantly different in two views and therefore the performance of person re-identification is degraded.In this paper,a feature fusion method for person re-identification is proposed including HSV color feature,color histogram feature and texture feature extracted by the histogram of oriented gradient descriptor.The specific process is divided into the training phase and the recognition phase.In the training phase,the feature descriptors of each image in the reference dataset are firstly extracted,and then a correlation matrix of the image features from two cameras is learned using canonical correlation analysis.As for re-identification,the feature descriptors of each image in the gallery dataset and the probe dataset are firstly extracted,and then they are transformed by the correlation matrix.Finally,re-identification is implemented by measuring the similarity between the gallery image descriptor and the probe image descriptor.Experimental results on three datasets show that the proposed method outperforms the state-of-art approaches.%行人再识别过程中,由于姿势和光照等因素的变化使不同相机中所得行人的外形具有明显变化,较难提取不变性特征,导致识别率偏低.鉴于此种情况,文中提出基于融合特征的行人再识别方法,提取的特征包括HSV颜色特征、颜色直方图特征及梯度方向直方图特征,行人再识别过程分为训练阶段和识别阶段.在训练阶段,首先对训练图像集中每幅图像进行特征提取,然后利用典型相关分析获得2部相机拍摄同一行人的图像特征之间的相关性,生成相关性矩阵.在识别阶段,首先对参考图像集和测试图像集中每幅图像进行特征提取,然后将各自特征向量利用相关性矩阵进行变换,最后进行相似度度量,得到识别结果.在3个图像库上的实验表明,文中方法可以提高行人再识别的识别率.
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