Consistency-Constancy Bi-Knowledge Learning for Pedestrian Detection in Night Surveillance

Pedestrian detection in the night surveillance is a challenging yet not largely explored task. As the success of the detector in the daytime surveillance and the convenient acquisition of all-weather data, we learn knowledge from these data to benefit pedestrian detection in night surveillance. We find two key properties of surveillance: distribution cross-time consistency and background cross-frame constancy. This paper proposes a consistency-constancy bi-knowledge learning (CCBL) for pedestrian detection in night surveillance, which is able to simultaneously achieve the night pedestrian detection's useful knowledge, coming from day and night surveillance. Firstly, based on the robustness of the existing detector in day surveillance, we obtain pedestrians' distribution in the daytime scene using the detector's detection results in the daytime scene. Based on the consistency of pedestrians' distribution during the day and night in the same scene, the pedestrian distribution from daytime is used as the consistency-knowledge for pedestrian detection in night surveillance. Secondly, the background as a constant knowledge of the surveillance scene is extractable and contributes to the division of the foreground, which contains most of the pedestrian regions and helps in pedestrian detection for night surveillance. Finally, we add bi-knowledge representation to promote each other and merge them together as the final pedestrian representation. Through extensive experiments, our CCBL significantly outperforms the state-of-the-art methods on public pedestrian detection datasets. In the NightSurveillance dataset, CCBL reduced the average missed detection rate by 3.04% compared to the existing best method.