Yuan Xin, He Ying, Hao Guozhu
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.
Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430065, China.
Sensors (Basel). 2025 Jan 31;25(3):862. doi: 10.3390/s25030862.
Person re-identification (Re-ID) aims to retrieve all images of the specific person captured by non-overlapping cameras and scenarios. Regardless of the significant success achieved by daytime person Re-ID methods, they will perform poorly due to the degraded imaging quality under low-light conditions. Therefore, some works attempt to synthesize low-light images to explore the challenges in the nighttime, which omits the fact that synthetic images may not realistically reflect the challenges of person Re-ID at night. Moreover, other works follow the "enhancement-then-match" manner, but it is still hard to capture discriminative identity features owing to learning enlarged irrelevant noise for identifying pedestrians. To this end, we propose a novel nighttime person Re-ID method, termed Feature Discovery Transformer (FDT), explicitly capturing the pedestrian identity information hidden in darkness at night. More specifically, the proposed FDT model contains two novel modules: the Frequency-wise Reconstruction Module (FRM) and the Attribute Guide Module (AGM). In particular, to reduce noise disturbance and discover pedestrian identity details, the FRM utilizes the Discrete Haar Wavelet Transform to acquire the high- and low-frequency components for learning person features. Furthermore, to avoid high-frequency components being over-smoothed by low-frequency ones, we propose a novel Normalized Contrastive Loss (NCL) to help the model obtain the identity details in high-frequency components for extracting discriminative person features. Then, to further decrease the negative bias caused by appearance-irrelevant features and enhance the pedestrian identity features, the AGM improves the robustness of the learned features by integrating the auxiliary information, i.e., camera ID and viewpoint. Extensive experimental results demonstrate that our proposed FDT model can achieve state-of-the-art performance on two realistic nighttime person Re-ID benchmarks, i.e., Night600 and RGBNT201rgb datasets.
行人重识别（Re-ID）旨在检索由非重叠摄像头和场景捕获的特定人员的所有图像。尽管白天行人Re-ID方法取得了显著成功，但在低光照条件下成像质量下降时，它们的表现会很差。因此，一些工作尝试合成低光照图像以探索夜间的挑战，但这忽略了合成图像可能无法真实反映夜间行人Re-ID挑战的事实。此外，其他工作遵循“增强然后匹配”的方式，但由于学习到用于识别行人的放大的无关噪声，仍然难以捕捉到有区分性的身份特征。为此，我们提出了一种新颖的夜间行人Re-ID方法，称为特征发现Transformer（FDT），用于明确捕捉夜间隐藏在黑暗中的行人身份信息。更具体地说，所提出的FDT模型包含两个新颖的模块：频率重建模块（FRM）和属性引导模块（AGM）。特别是，为了减少噪声干扰并发现行人身份细节，FRM利用离散哈尔小波变换获取高频和低频分量来学习行人特征。此外，为了避免高频分量被低频分量过度平滑，我们提出了一种新颖的归一化对比损失（NCL），以帮助模型在高频分量中获取身份细节，从而提取有区分性的行人特征。然后，为了进一步减少由外观无关特征引起的负偏差并增强行人身份特征，AGM通过整合辅助信息（即摄像头ID和视角）来提高所学习特征的鲁棒性。大量实验结果表明，我们提出的FDT模型在两个真实的夜间行人Re-ID基准测试（即Night600和RGBNT201rgb数据集）上可以实现最优性能。
