Fine-Grained Truck Re-identification: A Challenge

In intelligent transportation and smart city, truck re-identification (Re-ID) is a crucial task in controlling traffic violations of laws and regulations, especially in the absence of satellite positioning and license plate information. There are many specific fine-grained types in trucks compared to common person and vehicle Re-ID, which hinders the direct application of person and vehicle Re-ID methods to truck Re-ID. In this work, we contribute a new truck image dataset, named Truck-ID, for truck Re-ID specifically. The dataset contains 32,353 images of trucks from 7 monitoring sites of real traffic surveillance, including 13,137 license plate IDs. According to the difficulty of truck Re- ID, the gallery of Truck-ID dataset is further divided into three sub-datasets to evaluate the quality of different truck Re-ID models more comprehensively. Furthermore, we propose an effective Double Granularity Network (DGN) for truck Re-ID, which considers both global and local features of truck by focusing on truck head and body separately. Experiments show that DGN can effectively integrate global and local features to achieve robust fine-grained truck Re-ID. Our work provides a benchmark dataset for truck Re-ID and a baseline network for both research and industrial communities. The Truck-ID dataset and DGN codes are available at: https://pan.baidu.com/s/ 18Vc6NOiipGLLvcKj8U75Hw. Although the proposed DGN is relatively simple and easy to implement, it is effective in learning discriminative features of trucks and has remarkable performance in targeting truck re-identification. The Truck-ID dataset we made can promote the development of re-identification in the truck field.