Illumination-Invariant Person Re-Identification

Abstract

Due to the effect of weak illumination, person images captured by surveillance cameras usually contain various degradations such as color shift, low contrast and noise. These degradations result in severe discriminant information loss, which makes the person re-identification (re-id) more challenging. However, existing person re-identification approaches are designed based on the assumption that the pedestrians images are under well lighting conditions, which is impractical in real-world scenarios. Inspired by the Retinex theory, we propose a illumination-invariant person re-identification framework which is able to simultaneously achieve Retinex illumination decomposition and person re-identification. We first verify that directly using weak illuminated images can greatly reduce the performance of person re-id. We then design a bottom-up attention network to remove the effect of weak illumination and obtain the enhanced image without introducing over-enhancement. To effectively connect low-level and high-level vision tasks, a joint training strategy is further introduced to boost the performance of person re-id under weak illumination conditions. Experiments have demonstrated the advantages of our method on benchmarks with severe lighting changes and low light conditions.