Orientation-Aware Vehicle Re-identification via Synthesis Data Orientation Regression

The chapter focuses on the critical task of vehicle re-identification (re-ID), a fundamental technology in smart city construction and surveillance systems. Traditional approaches using hardware sensors are costly and impractical for large-scale deployment. Vision-based methods, particularly deep feature-based re-ID, have become mainstream. However, extreme orientation variations in vehicle images pose significant challenges to recognition accuracy. To address this, the authors propose a method that utilizes synthetic data generated by the VehicleX engine to train orientation regression models. These models extract orientation-aware features, which are then fused with general re-ID features to eliminate orientation bias and enhance retrieval performance. Extensive experiments on two large-scale datasets, VeRi-776 and VehicleID, demonstrate the effectiveness of this approach, outperforming previous state-of-the-art models. The chapter also provides a comprehensive review of related work and a detailed explanation of the methodology, making it a valuable resource for professionals seeking to advance the field of vehicle re-ID.