A Strong Baseline for Vehicle Re-Identification
This improves vehicle tracking for traffic management systems, but is incremental as it builds on existing challenge datasets and methods.
The paper tackles vehicle re-identification by analyzing performance bottlenecks and proposing solutions including domain gap reduction, network modifications with attention, and adaptive loss weighting, achieving 61.34% mAP on CityFlow and 87.1% mAP on Veri benchmarks.
Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://github.com/cybercore-co-ltd/track2_aicity_2021.