MultiSpectral Pedestrian Detection

Multispectral pedestrian detection aims to improve pedestrian detection accuracy and robustness, particularly in challenging conditions like low light, by fusing information from visible (RGB) and thermal (infrared) cameras. Current research focuses on efficient fusion strategies, including transformer-based architectures and novel attention mechanisms, to minimize computational cost while maximizing performance. These advancements are crucial for real-time applications such as autonomous driving and surveillance systems, where reliable pedestrian detection is paramount for safety and security. The field is actively addressing issues like modality bias and the efficient integration of multimodal data to achieve superior accuracy and generalization.