Fall detection in low-illumination environment with adaptive image enhancement

Under low-light conditions, existing RGB frame-based fall detection methods suffer from a significant decline in accuracy. To address this challenge, this paper proposes an innovative fall detection approach that exclusively utilizes RGB frames while incorporating adaptive image enhancement to improve performance. The proposed method leverages the Deep Deterministic Policy Gradient (DDPG) algorithm to estimate illumination conditions in the captured frames. By learning illumination characteristics, the DDPG algorithm accurately predicts parameters for image enhancement. Advanced techniques are then applied to adjust brightness and contrast, producing high-quality visuals even in dim environments. The effectiveness of this approach is validated using the YOLOv5 object detection algorithm to detect falls in both the original low-light images and their enhanced counterparts. Experimental results show that the proposed method significantly outperforms baseline approaches in low-light settings while maintaining real-time performance and robustness, offering a promising solution for fall detection.