Looking GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention
This work addresses vehicle re-identification for surveillance systems, presenting an incremental improvement with a more compact and efficient model.
The paper tackles vehicle re-identification in heterogeneous camera networks by proposing GLAMOR, a unified model that simultaneously extracts global and local features using attention modules, achieving state-of-the-art performance with mAPs of 80.34, 76.48, and 77.15 on VeRi-776, VRIC, and VeRi-Wild datasets.
Vehicle re-identification (re-id) is a fundamental problem for modern surveillance camera networks. Existing approaches for vehicle re-id utilize global features and local features for re-id by combining multiple subnetworks and losses. In this paper, we propose GLAMOR, or Global and Local Attention MOdules for Re-id. GLAMOR performs global and local feature extraction simultaneously in a unified model to achieve state-of-the-art performance in vehicle re-id across a variety of adversarial conditions and datasets (mAPs 80.34, 76.48, 77.15 on VeRi-776, VRIC, and VeRi-Wild, respectively). GLAMOR introduces several contributions: a better backbone construction method that outperforms recent approaches, group and layer normalization to address conflicting loss targets for re-id, a novel global attention module for global feature extraction, and a novel local attention module for self-guided part-based local feature extraction that does not require supervision. Additionally, GLAMOR is a compact and fast model that is 10x smaller while delivering 25% better performance.