Abstract Person re‐identification represents a pivotal sub‐problem in image retrieval, boasting broad application prospects in fields such as intelligent security and video surveillance. However, most existing person re‐identification methods predominantly focus solely on visual features pertaining to the person targets, thereby disregarding some supporting information closely related to the scene context. In the context of athlete re‐identification during sports event scenes, the athlete bib number is fully considered, an important clue that can provide different athletes' identities, and the traditional visual features of the person and high‐level semantic information of the bib number text are fused. A multi‐source information mutual gain mechanism is designed to improve the accuracy of the person re‐identification task. In the existing only publicly available marathon bib number dataset RBNR, the recognition accuracy of this method is significantly superior to that of the existing person re‐identification method. In addition, this paper constructs and publishes an athlete re‐identification dataset (HNNU‐ReID8000) for mainstream sports events, and the mean average precision (mAP) value of this method reaches 96.1% on this dataset, significantly ahead of existing state‐of‐the‐art person re‐identification methods. The code and the HNNU‐ReID8000 dataset will be released at https://github.com/yanbin‐zhu/zyb_person‐reid .

Traffic text detection is crucial for traffic scene understanding in intelligent transportation systems (ITS). Although natural scene text detection has been extensively studied, yielding noteworthy results, little research has focused on traffic text detection. Traffic text, as a special type of natural scene text, faces not only the general challenges of natural scene text detection but also the significant impact of false alarms from non-traffic texts on system performance. In light of these challenges, we propose an end-to-end traffic text detection framework that can effectively detect traffic text captured by in-vehicle cameras in various driving scenarios. The key contributions of our proposed approach are: (1) an Image Enhancement Module designed to remove fog and enhance low-quality images; (2) a plug-and-play Text Feature Enhancement Module; (3) a Joint Loss Function; and (4) the creation of an open driving environment traffic text dataset (named ODETT-3000) containing various traffic environments. Comprehensive experimental studies have verified that our method achieves state-of-the-art performance on traffic text datasets such as CTST-1600, TPD, and ODETT-3000, as well as promising results on public natural scene text datasets such as MSRA-TD500, ICDAR 2015, and CTW 1500, thereby showcasing the superior performance and adaptability of our method. The code and our dataset (ODETT-3000) will be available athttps://github.com/yanbin-zhu/TTDNet.

Pedestrian reidentification (ReID) is a challenging task that involves identifying and retrieving specific pedestrians across different cameras and scenes. This problem has significant implications for security surveillance, and has thus received substantial attention in recent years. However, traditional convolutional neural networks (CNNs) have limited receptive fields and cannot capture global information. Moreover, transformer networks, which excel in long-range feature capture, are prone to accuracy degradation due to loss of details. To address these limitations, we propose a transformer-based pedestrian ReID network with double-branch information mutual gain (DIMGNet), which leverages hierarchical parallel levels to support multi-granularity feature information mutual gain. Our model also incorporates an auxiliary camera information (ACI) module to improve feature representation ability. We further embed a cross-attention mechanism into the architecture to enhance mutual gain between multi-granularity features and improve feature discrimination. Finally, we introduce a shuffling technique to increase the robustness of the extracted features. We evaluate the proposed method on several benchmark datasets, including Market-1501[1], MSMT17[2], DukeMTMC-reID [3], and Occluded-Duke [4], achieving$mAP$values of 90.7%, 68.4%, 83.7%, and 60.6%, respectively. Our method outperforms most state-of-the-art methods, demonstrating the effectiveness of our method. The code will be publicly released athttps://github.com/ZhenlinZhu/DIMGNet.

As one of the basic tasks of computer vision, semantic segmentation is widely used in many fields, e.g., medical images parsing, scene parsing, autonomous driving, etc. In the current mainstream approaches, downsampling or patching operation is required to ensure that the GPU memory is not overloaded for dealing with the high-resolution input images. However, the corresponding cost is the lack of details in the final segmentation map. In this work, we proposed RFNet, a refinement network which resolves the lack of detailed information in coarse predictions by fusing the coarse predictions and the fine predictions gained by fine input image patches. There are three key characteristics: (i) designing a spatial information extraction module which can efficiently process information in coarse and fine feature maps at spatial level. (ii) proposing an auxiliary-fusion information branch calculated from the prediction maps, which contribute to refine predictions. (iii) designing a boundary auxiliary loss function in the training process, which makes the model pay more attention to those pixels belonging to the boundary of objects. We show the superiority of the proposed RFNet on the Cityscapes dataset, the experimental results illustrate that ours RFNet performance outperforms other state-of-the-art approaches with low computation consumption. The codes will be available at: https://github.com/zhu-gl-ux/RFNet.