Smart City Gnosys

Smart city article details

Title	V2Reid: Vision-Outlooker-Based Vehicle Re-Identification
ID_Doc	60777
Authors	Qian Y.; Barthelemy J.; Iqbal U.; Perez P.
Year	2022
Published	Sensors, 22, 22
DOI	http://dx.doi.org/10.3390/s22228651
Abstract	With the increase of large camera networks around us, it is becoming more difficult to manually identify vehicles. Computer vision enables us to automate this task. More specifically, vehicle re-identification (ReID) aims to identify cars in a camera network with non-overlapping views. Images captured of vehicles can undergo intense variations of appearance due to illumination, pose, or viewpoint. Furthermore, due to small inter-class similarities and large intra-class differences, feature learning is often enhanced with non-visual cues, such as the topology of camera networks and temporal information. These are, however, not always available or can be resource intensive for the model. Following the success of Transformer baselines in ReID, we propose for the first time an outlook-attention-based vehicle ReID framework using the Vision Outlooker as its backbone, which is able to encode finer-level features. We show that, without embedding any additional side information and using only the visual cues, we can achieve an 80.31% mAP and 97.13% R-1 on the VeRi-776 dataset. Besides documenting our research, this paper also aims to provide a comprehensive walkthrough of vehicle ReID. We aim to provide a starting point for individuals and organisations, as it is difficult to navigate through the myriad of complex research in this field. © 2022 by the authors.
Author Keywords	explainable AI; secure AI; smart cities; vehicle re-identification; Vision Outlooker

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