Scalable Vehicle Re-Identification via Self-Supervision

TL;DR

This paper introduces SSBVER, a self-supervised vehicle re-identification method that balances accuracy and efficiency, achieving state-of-the-art performance with minimal computational overhead and broad compatibility across architectures.

Contribution

The paper presents a simple, effective self-supervised approach for vehicle re-identification that maintains high accuracy without complex modules or extra inference costs.

Findings

Achieves state-of-the-art accuracy with a single network during inference

Generalizes well across different backbone architectures

Significantly boosts accuracy with minimal computational overhead

Abstract

As Computer Vision technologies become more mature for intelligent transportation applications, it is time to ask how efficient and scalable they are for large-scale and real-time deployment. Among these technologies is Vehicle Re-Identification which is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. To balance the demands of accuracy and computational efficiency, in this work we propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time and is free of intricate and computation-demanding add-on modules often seen in state-of-the-art approaches. Through extensive experiments, we show our approach, termed Self-Supervised and Boosted VEhicle Re-Identification (SSBVER), is on par with state-of-the-art alternatives in terms of accuracy without introducing any additional overhead during deployment. Additionally we show that our approach, generalizes to different backbone architectures which facilitates various resource constraints and consistently results in a significant accuracy boost.