Occluded Pedestrian

Occluded pedestrian detection focuses on improving the accuracy and robustness of automated systems, such as self-driving cars, in identifying pedestrians partially or fully hidden from view. Current research emphasizes developing advanced deep learning models, often based on YOLO architectures or similar one-stage detectors, incorporating multimodal data fusion (e.g., combining camera and LiDAR data), and employing techniques like adversarial feature completion or temporal information integration to address the challenges posed by occlusion. Successfully addressing this challenge is crucial for enhancing the safety and reliability of autonomous vehicles and other applications requiring robust pedestrian detection in complex environments.