An improved deep learning architecture for person re identification
In this work, we propose a method for simultaneously learning features and a corresponding similarity metric for person re identification. We present a deep convolutional architecture with layers specially designed to address the problem of re identification. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. Novel elements of our architecture include a layer that computes cross input neighborhood differences, which capture local relationships between the two input images based on mid level features from each input image. A high level summary of the outputs of this layer is computed by a layer of patch summary features, which are then spatially integrated in subsequent layers. Our method significantly outperforms the state of the art on both a large data set (CUHK03) and a medium sized data set (CUHK01) and is resistant to over fitting. We also demonstrate that by initially training on an unrelated large data set before fine tuning on a small target data set, our network can achieve results comparable to the state of the art even on a small data set (VIPeR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)