Learning local embedding deep features for person re-identification in camera networks

In this paper, we propose a novel feature learning method named local embedding deep features (LEDF) for person re-identification in camera networks. In order to learn the structural information of pedestrian, we first utilize the verification network that does not require explicit identity labels to obtain the local summing maps. We then combine all local summing maps of a pedestrian image to form the holistic summing map which has the same identity label with the original pedestrian image. Finally, we take the holistic summing maps as the input to train the identification network, and then obtain the LEDF from the last fully connected layer. The proposed LEDF fully considers the structural information by learning the local features and meanwhile possesses strong discriminative ability by learning global features. The experimental results on two large-scale datasets (Market-1501 and CUHK03) demonstrate that the proposed LEDF achieves better results than the state-of-the-art methods.