Attributes Guided Feature Learning for Vehicle Re-identification

Understand

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance.

• However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with the similar appearance.
• To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID.
• In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes (i.e., camera view, vehicle type and vehicle color).