Abstract: Vehicle re-identification (re-ID) is an area that has received far less attention in the computer vision community than the prevalent person re-ID. Possible reasons for this slow progress are the lack of appropriate research data and the special 3D structure of a vehicle. Previous works have generally focused on some specific views (e.g. front), but these methods are less effective in realistic scenarios where vehicles usually appear in arbitrary views to cameras. In this paper, we focus on the uncertainty of vehicle viewpoint in re-ID, proposing two end-to-end deep architectures: the Spatially Concatenated ConvNet (SCCN) and CNN-LSTM Bi-directional Loop (CLBL).
Our models exploit the great advantages of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to learn transformations across different viewpoints of vehicles. Thus, a multi-view vehicle representation containing all viewpoints’ information can be inferred from the only one input view, and then used for learning to measure distance. To verify our models, we also introduce a Toy Car RE-ID dataset with images from multiple viewpoints of 200 vehicles. We evaluate our proposed methods on the Toy Car RE-ID dataset and the public Multi-View Car, VehicleID and VeRi datasets. Experimental results illustrate that our models achieve consistent improvements over state-of-the-art vehicle re-ID approaches.