Nighttime Pedestrian Detection

Nighttime pedestrian detection aims to reliably identify pedestrians in low-light conditions, a crucial challenge for autonomous driving and advanced driver-assistance systems. Current research focuses on improving detection accuracy by incorporating background information into attention mechanisms, leveraging unsupervised domain adaptation techniques to bridge the gap between day and night image data, and utilizing multi-spectral image fusion (e.g., combining infrared and visible light) to mitigate the effects of poor lighting and glare. These advancements are vital for enhancing the safety and reliability of autonomous vehicles and other applications requiring robust nighttime object recognition.