People re-identification using deep convolutional neural network

One key issue for people re-identification is to find good features or representation to bridge the gaps among different appearances of the same people, which is introduced by large variances in view point, illumination and non-rigid deformation. In this paper, we create a deep convolutional neural network (deep CNN) to solve this problem and integrate feature learning and re-identification into one framework. In order to deal with such ranking-like comparison problem, we introduce a linear support vector machine (linear SVM) to replace conventional softmax activation function. Instead of learning cross-entropy loss, we adopt a margin-based loss of pair-wise image to measure the similarity of the comparing pair. Although the proposed model is quite simple, the experimental result shows encouraging performance of our method.