Unsupervised metric learning for person re-identification by image re-ranking

Abstract

Person re-identification is an important and challenging problem in video surveillance. Most of the methods are based on supervised distance metrics learning, which have to label samples for distance metrics learning, while it is hardly done in massive cameras. This paper carried out an unsupervised distance metric learning method based on image re-ranking. This method calculates original distance matrix for samples from two cameras using original distance metric function, and re-ranking distance matrix to acquire a better distance function, then new distance rank matrix is calculated. This matrix is used to label positive and negative samples automatically, doing unsupervised distance metric learning, and acquire a better Mahalanobis distance metric function, without the need to manually label samples. Experiments are done on public datasets, and result is evaluated by CMC, which indicates this algorithm could overcome the difficulties of labelling massive samples, with a better re-identification rate than other algorithms.