Low-light pedestrian detection from RGB images using multi-modal knowledge distillation

While deep learning based pedestrian detection systems have continued to scale new heights in recent times, the performance of such algorithms tends to degrade under challenging illumination conditions. This causes a bottleneck in ready portability of such systems to Advanced Driver Assistance Systems (ADAS), where consistent performance across varying environmental lighting is desired. Inspired by the concept of dark knowledge, this paper proposes a novel guided deep network that distills knowledge from a multi-modal pedestrian detector. The proposed network learns to extract both RGB and thermal-like features from RGB images alone, thus compensating for the requirement of significantly costly automotive-grade thermal cameras. Compelling detection performance in severe lighting conditions is demonstrated on a publicly available night-time pedestrian dataset — KAIST. We achieve an effective miss-rate of 12% lower than the recent state-of-the-art methods.