# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 1 Introduction  
With the rapid increase in number of vehicles, the establishment of intelligent transportation systems (ITS) has become increasingly essential. Vehicle re-identification plays a crucial role in ITS by aiming to retrieve all the images of a given query vehicle identity [1, 2, 3, 4, 5, 6, 7], contributing significantly to applications such as suspicious vehicle tracking, vehicle event detection, and vehicle counting [8]. While license plates are commonly used identifiers for vehicles, real-world road surveillance systems often face challenges such as challenging angles and lighting conditions, making accurate license plate recognition difficult. Additionally, intentional obstruction, forgery, and privacy concerns associated with license plates pose significant challenges for vehicle re-identification using them [9]. Consequently, the visual features of vehicles are explored as an alternative to license plates for vehicle re-identification. With the significant progress of deep learning in computer vision, many methods [10, 11, 12, 13, 14, 15, 16, 17, 18] now utilize neural networks to adaptively extract high-level features from vehicle images, making them a primary and efficient approach for vehicle re-identification. Some methods [10, 11, 12, 13] focus on embedding a vehicle image into a global feature using CNNs. Nevertheless, they underperform when different vehicles share significantly similar appearance attributes (e.g., Figure 0(a)) or when the same vehicle exhibits diverse appearances (e.g., Figure 0(b)), namely, the inter-class similarity and the intra-class difference issues. Asshown in Figure 1a, the two different vehicles are difficult to distinguish by their overall appearance but can be by local details. Therefore, most methods [14; 15; 16; 19; 20; 21; 22; 23] extract local features from regions and combine them with the global feature to enhance vehicle representation. Still, as depicted in Figure 1b, the regions of local details in different images of the same vehicle do not always correspond, which means that their local features are not inherently aligned. Thus, when local features are conventionally used during the inference stage, irrelevant information caused by misalignment may be introduced. Furthermore, crudely combining the global feature with local features will result in redundancy, as there is some overlap in information between them. In addition to visual features, other cues are essential to provide complementary information for vehicle re-identification. Unlike the random movement of humans, the movement of vehicles in real-world traffic scenarios is restricted by road topology and traffic rules. This suggests that we can filter vehicle images with the help of spatial-temporal information to achieve more robust vehicle re-identification. Although some methods [24; 25; 26; 10; 27] have attempted to use statistical means to model spatial-temporal relations or constraints to refine the retrieval results of vehicles, there is still a need for a comprehensive exploration of both spatial-temporal pattern mining and its integration with visual features. Considering the above-mentioned issues, we proposed a stripe-assisted global transformer (SaGT) method that guides the global feature to focus on discriminative details from local region representations. Concretely, we design a dual-branch architecture to learn global and local features based on the pure vision transformer (ViT). Following the ViT, we first split the images into patches and embed them with a transformer encoder. Then, we design a stripe feature module (SFM) in one branch to construct stripe-based features and employ a transformer layer to obtain a global representation in the other one. To encourage the model to learn regional features to enrich the whole representation, we further design a stripe-assisted global loss (SaGL). In this way, we can only use the global feature with key parts' details for vehicle re-identification, avoiding the redundancy and misalignment problems of fusing local features.