VEHICLE RE-IDENTIFICATION TECHNIQUES USING NEURAL NETWORKS FOR IMAGE ANALYSIS, VIEWPOINT-AWARE PATTERN RECOGNITION, AND GENERATION OF MULTI-VIEW VEHICLE REPRESENTATIONS

摘要

This disclosure relates to improved vehicle re-identification techniques. The techniques described herein utilize artificial intelligence (Al) and machine learning functions to re-identify vehicles across multiple cameras. Vehicle re-identification can be performed using an image of the vehicle that is captured from any single viewpoint. Attention maps may be generated that identify regions of the vehicle that include visual patterns that overlap between the viewpoint of the captured image and one or more additional viewpoints. The attention maps are used to generate a multi-view representation of the vehicle that provides a global view of the vehicle across multiple viewpoints. The multi-view representation of the vehicle can then be compared to previously captured image data to perform vehicle re-identification.