Single-Modal Pedestrian Detection Leveraging Multimodal Knowledge for Blackout Situations

Seungho Shin, Jung Uk kim

http://doi.org/10.5626/JOK.2024.51.1.86

Multispectral pedestrian detection using both visible and thermal data is an actively researched topic in the field of computer vision. However, the majority of the existing studies have only considered scenarios where the camera operates without challenges, leading to a significant decline in performance when a camera blackout happens. Recognizing the importance of addressing the camera blackout challenge in multispectral pedestrian detection, this paper researched models that remain robust even during camera blackouts. Our model, proposed in this study, utilizes the Feature Tracing Method during training phase to apply the knowledge from multiple modalities to single-modal pedestrian detection. Even if the camera experiences a blackout and only one modality is input, the model predicts and operates as if it"s using multiple modalities. Through this approach, pedestrian detection performance in blackout situations is improved.