Enhanced Pedestrian Detection Model Transfer-Trained on YOLOv8 Using DenseFused RGB and FIR Images

There are broad benefits to developing pedestrian spaces in terms of environment, culture, and economy. Given this context, accurately measuring pedestrian traffic is considered a critical indicator of sidewalk usage. Currently, the mainstream method for detecting pedestrians utilizes RGB camera footage installed along sidewalks. However, especially during nighttime and adverse weather conditions, insufficient lighting hampers detection accuracy. In contrast, Far-Infrared(FIR) imaging does not require a light source as it measures radiated heat. This study proposes a pedestrian detection and tracking model that integrates the strengths of both RGB and FIR cameras through image fusion processing. Specifically, pedestrians are detected from fused images using a pedestrian detector and then tracked using a tracking system to measure pedestrian counts. Additionally, a version of the pedestrian detector trained on the fused images through transfer learning is developed, and its detection results are compared with those from a non-transfer learning model. The experimental results demonstrate that using fused images for detection and tracking is effective in specific data scenarios, confirming the utility of the image fusion model under varied conditions.