Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information

Abstract

Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID dataset.