Evaluating the Impact of Low-Light Image Enhancement Methods on Runner Re-Identification in the Wild

Abstract

Person re-identification (ReID) is a trending topic in computer vision. Significant developments have been achieved, but most rely on datasets with subjects captured statically within a short period of time in rather good lighting conditions. In the wild scenarios, such as long-distance races that involve widely varying lighting conditions, from full daylight to night, present a considerable challenge. This issue cannot be addressed by increasing the exposure time on the capture device, as the runners' motion will lead to blurred images, hampering any ReID attempts. In this paper, we survey some low-light image enhancement methods. Our results show that including an image processing step in a ReID pipeline before extracting the distinctive body appearance features from the subjects can provide significant performance improvements.