Vehicle Re-Identification by Deep Feature Fusion Based on Joint Bayesian Criterion

Vehicle re-identification is a challenging task as the differences between vehicles of the same model are extremely small. In this paper, we propose to fuse deep features extracted by two different CNNs for vehicle re-identification. CNNs can extract discriminative features for classification tasks. Features extracted by different CNNs describe different aspects of the input image, and are complementary to each other. We propose a new loss function called the Joint Bayesian loss to fuse the different deep features. The proposed Joint Bayesian loss can minimize the intra-class variations and simultaneously maximize the inter-class variations of the fused features, and it is very fit for the vehicle re-identification. Experiments on a large-scale vehicle dataset demonstrate the effectiveness of the proposed method.