Person re-identification based on multi-scale convolutional network

Person re-identification (re-ID) has become increasingly popular in the community due to its application and research significance. In the early days, hand-crafted algorithms and small-scale evaluation were predominantly reported. Recently, with the success of deep learning methods on many computer vision tasks, researchers started to put their focuses on learning high-performance features. In this paper, we propose a method by fusing features learned from a multi-scale convolutional neural network and the traditional hand-crafted features, which improves the performance significantly. The Shinpuhkan2014dataset has been selected as the training data, and we compare the proposed method on VIPeR, PRID, iLIDS and CUHK03 datasets. The experimental results show that the performance of the proposed method is superior to the current methods which have a training step on the testing sets.