Person Re-identification Using Two-Stage Convolutional Neural Network

Yonghui Zhang, Jie Shao, Deqiang Ouyang, Heng Tao Shen

Abstract: Person re-identification is a fundamental task in automated video surveillance and has been an area of intensive research in the past few years. Several person re-identification methods based on deep learning have been proposed and achieved remarkable performance. However, extraction of more useful spatial and temporal information from input images and design of a more effective approach to match the same persons are still challenging. In this paper, we present a novel Two-Stage Convolution Neural Network (TSCNN), which effectively extracts the spatio-temporal feature with two-stream network in two directions, and matches the person with a novel convolutional neural network. Extensive experiments are conducted on three public benchmarks, i.e., iLIDS-VID, PRID2011 and MARS datasets. The experimental results demonstrate that the performance of our TSCNN is better in comparison with the state-of-the-art methods. The code of TSCNN is available at https://github.com/zyoohv/TSCNN.