Attention-Aware Network and Multi-Loss Joint Training Method for Vehicle Re-Identification

Vehicle ReID is a core technology in Intelligent Traffic System (ITS) and an important branch of ReID problem. However, due to the complexity of the traffic environment, the dataset commonly has serious imbalances between positive and negative sample data. To conquer these issues, a novel effective CNN is proposed in this paper. Firstly, we present an Attention-Aware Network that combines the Spatial Attention Model with the Channel Attention Model to make the network focus on high importance areas and further improve the ability to identify matching vehicles. Besides, we propose the Multi-Loss Joint Training strategy to handling the data imbalances. Then to prove the effectiveness of our proposed method. We evaluated our network on the most popular VeRi-776 dataset. Abundant experiment results have shown the effectiveness of our proposed method in vehicle re-identification compared with existing method.