Image-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking

Quang Truong, Class of 2022
2020 The Boller Review  
Vehicle re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation to extract more features for false positive image exclusion. In this paper, we propose a model powered by adaptive attention modules that requires fewer label annotations but still out-performs the previous models. We also include a re-ranking method that takes account of the
more » ... ortance of metadata feature embeddings in our paper. The proposed method is evaluated on CVPR AI City Challenge 2020 dataset and achieves mAP of 37.25% in Track 2.
doi:10.18776/tcu/br/5/130 fatcat:rdgee7qum5dsdou2zlabcaetpi