Multi-view Vehicle Re-Identification Method Based on Siamese Convolutional Neural Network Structure

Abstract

Vehicle re-identification is a popular issue in intelligent traffic research. A lot of proposed method achieve vehicle re-identification by recognizing their license plate, because of the uniqueness. However, license plate can be stolen and pinned to different vehicles by criminals to hide their identities. In addition, the license plate number might be covered by dirt or stain, even hided from different viewpoints, makes the character recognition result might be wrong or unrecognized. To get more robust re-identification result, not only the license plate should be considered, but also the appearance. In this paper, we adopt Siamese Convolutional Neural Network structure, take license plate and vehicle appearance as input, come up with a neural network for vehicle re-identification task. We validate our proposed method on VeRi-776 dataset, and proof that it can deal with vehicle re-identification task well, even under variant viewpoints scenarios.