Vehicle Re-identification in Multi-branches Network

Abstract

Vehicle re-identification (Re-ID) aims to solve the problem of matching and identifying the same vehicles under the scene of cross multiple surveillance cameras. Finding the target vehicle quickly and accurately in the massive vehicle database is extremely important for public security, traffic surveillance and applications on smart city. However, it is very challenging due to the orientation variations, illumination changes, occlusion, low resolution, rapid vehicle movement, and amounts of similar vehicle models. In order to overcome these problems and improve the accuracy of vehicle re-identification, a multi-branches network is proposed, which is integrated by global-local feature fusion, channel attention mechanism, and weighted local feature. First, the fusion of global and local features is to obtain more complete information of the vehicle and enhance the learning ability of the model; second, the purpose of embedding the channel attention module in the feature extraction branch is to extract the personalized feature of the vehicle; finally, the influence of sky area and noise information on feature extraction is weakened by weighted local feature. The comprehensive experiments implemented on the mainstream evaluation datasets including VeRi-776, VRIC, and VehicleID indicate that our method can effectively improve the accuracy of vehicle re-identification and is superior to the state-of-the-art methods.