Joint Multiple Fine-grained feature for Vehicle Re-Identification

Abstract

The process of recognizing the same vehicle in different scenes is called vehicle re-identification. However, due to the different locations of the surveillance cameras, there may be obstacles in the captured vehicle pictures and multiple viewpoints may make the same vehicle look different. In order to effectively reduce the interference of obstacle occlusion, multiple viewpoints, and other factors on vehicle re-identification, in this paper, we propose a multi-fine-grained feature extraction network. While retaining the global information of vehicles, we extract the finegrained features of vehicles precisely by segmenting the vehicle feature map. In addition, we introduce a new evaluation metric mean Inverse Negative Penalty (mINP) to evaluate the vehicle re-identification model more comprehensively. Our method achieves superior accuracy over the state-of-the-art methods on the challenging vehicle datasets: VeRi-776, VehicleID, and VRIC.

Keywords

• Image retrieval
• Deep learning
• Vehicle re-identification
• Fine-grained feature
• Feature map segmentation
• mINP