关键词: 行人重识别, 深度学习, 局部特征, 多尺度滑动窗口

Abstract: In this paper, a local region selection and local feature extraction algorithm based on deep learning is proposed for human pose variation, alignment and partial occlusion in the person re-identification problem. The algorithm firstly obtains the basic features by the residual convolutional neural network, then extracts the features of different candidate local regions by the multi-scale sliding windows, and groups them according to their coverage area. Each group selects an optimal local feature and merges the global features to obtain the final feature representation. The experimental results show that the local features extracted by this method are more representative and improve the accuracy of person re-identification.

Key words: person re-identification, deep learning, local feature, multi-scale sliding window