High Confidence Attribute Recognition For Vehicle Re-Identification

Abstract

Vehicle re-identification aims to associate images or videos of the same vehicle collected from different cameras. Many existing methods address the vehicle re-identification problem by explicitly learning distinguishable global features. However, vehicle attributes, i.e., logo category and orientation, play an indispensable role in identifying vehicles. In this paper, we first propose deep models to recognize vehicle attributes. Then, based on these attributes, we adopt a High Confidence Attribute Network (HCANet) to extract weighted global features. A comprehensive evaluation on the Vehi-cleID dataset shows that our approach achieves competitive results.

Partition and Reunion: A Viewpoint-Aware Loss for Vehicle Re-Identification

“…Vehicle Re-identification (ReID) [1][2][3] has received a great deal of attention recently due to practical applications in urban surveillance, i.e., cross-camera tracking [4,5] and multicamera behavior analysis [6,7]. It aims to retrieve vehicle images in a large camera network, where the target images have the same identity and come from multiple cameras.…”