Deep full-scaled metric learning for pedestrians re-identification: A pre-requisite study on multi-camera-based affective computing

In this study, a new full-scaled deep discriminant model is proposed to tackle the re-identification (re-id) problem of pedestrian targets, which aims to identify pedestrian targets within a network of cameras with non-overlapping fields of view and is pre-requisite in multi-camera-based affective computing. The new full-scaled model is realized by taking concepts of depth, width, and cardinality simultaneously into consideration, and the challenging re-id problem in this study is further tackled via a novel deep semi-supervised metric learning method based on the full-scaled model. Additionally, both the conventional stochastic gradient descent algorithm and an alternative more efficient proximal gradient descent algorithm are derived to realize the new deep metric learning method. For experimental evaluations, the novel full-scaled deep metric learning method has been compared with 9 other popular re-id methods based on 3 well-known databases. Comprehensive statistical analyses suggest the superiority of the new method when handling the balance learning problem in the re-id task.