A Novel Approach to Pedestrian Re-Identification in Low-Light and Zero-Shot Scenarios: Exploring Transposed Convolutional Reflectance Decoders

In recent years, pedestrian re-identification technology has made significant progress, with various neural network models performing well under normal conditions, such as good weather and adequate lighting. However, most research has overlooked extreme environments, such as rainy weather and nighttime. Additionally, the existing pedestrian re-identification datasets predominantly consist of well-lit images. Although some studies have started to address these issues by proposing methods for enhancing low-light images to restore their original features, the effectiveness of these approaches remains limited. We noted that a method based on Retinex theory designed a reflectance representation learning module aimed at restoring image features as much as possible. However, this method has so far only been applied in object detection networks. In response to this, we improved the method and applied it to pedestrian re-identification, proposing a transposed convolution reflectance decoder (TransConvRefDecoder) to better restore details in low-light images. Extensive experiments on the Market1501, CUHK03, and MSMT17 datasets demonstrated that our approach delivered superior performance.