Similarity Learning with Listwise Ranking for Person Re-Identification

Person re- identification is an important task in video surveillance systems. It consists in matching an image of a probe person among a gallery image set of people detected from a network of surveillance cameras with non-overlapping fields of view. The main challenge of person re- identification is to find image representations that are discriminating the persons' identities and that are robust to the viewpoint, body pose, illumination changes and partial occlusions. In this paper, we proposed a metric learning approach based on a deep neural network using a novel loss function which we call the Rank- Triplet loss. This proposed loss function is based on the predicted and ground truth ranking of a list of instances instead of pairs or triplets and takes into account the improvement of evaluation measures during training. Through our experiments on two person re- identification datasets, we show that the new loss outperforms other common loss functions and that our approach achieves state-of-the-art results on these two datasets.