Roadside Camera

Roadside cameras are increasingly used for intelligent transportation systems, aiming to enhance traffic safety and efficiency through automated perception and analysis. Current research focuses on developing robust computer vision models, including convolutional neural networks and vision transformers, to perform tasks such as 3D object detection, road surface friction estimation, and driver behavior characterization from video data. These advancements leverage techniques like bird's-eye-view projections, ground-aware embeddings, and multi-modal sensor fusion (e.g., combining camera and LiDAR data) to improve accuracy and reliability. The resulting data contributes to improved autonomous vehicle navigation, traffic flow optimization, and enhanced road safety measures.