Complete Solution for Vehicle Re-ID in Surround-view Camera System

TL;DR

This paper presents a comprehensive vehicle re-identification method for surround-view camera systems in autonomous vehicles, addressing fisheye distortion and multi-camera appearance variations to achieve high accuracy and real-time performance.

Contribution

It introduces an integrative Re-ID approach combining tracking consistency and attention-based neural networks with spatial constraints for surround-view systems.

Findings

Achieves state-of-the-art accuracy in vehicle Re-ID

Operates in real-time suitable for autonomous driving

Provides a new annotated fisheye dataset for research

Abstract

Vehicle re-identification (Re-ID) is a critical component of the autonomous driving perception system, and research in this area has accelerated in recent years. However, there is yet no perfect solution to the vehicle re-identification issue associated with the car's surround-view camera system. Our analysis identifies two significant issues in the aforementioned scenario: i) It is difficult to identify the same vehicle in many picture frames due to the unique construction of the fisheye camera. ii) The appearance of the same vehicle when seen via the surround vision system's several cameras is rather different. To overcome these issues, we suggest an integrative vehicle Re-ID solution method. On the one hand, we provide a technique for determining the consistency of the tracking box drift with respect to the target. On the other hand, we combine a Re-ID network based on the attention mechanism with spatial limitations to increase performance in situations involving multiple cameras. Finally, our approach combines state-of-the-art accuracy with real-time performance. We will soon make the source code and annotated fisheye dataset available.