Pedestrian Tracking

Pedestrian tracking aims to automatically identify and follow individuals' movements in video or sensor data, crucial for applications like traffic management and autonomous driving. Current research emphasizes robust tracking in challenging conditions (occlusion, crowding, varying viewpoints) using diverse sensor modalities (cameras, LiDAR, audio) and advanced algorithms such as Kalman filters, neural operators, and deep learning architectures (e.g., convolutional neural networks, recurrent neural networks, and attention mechanisms) to improve accuracy and efficiency. These advancements are driving improvements in urban planning, public safety, and human-robot interaction by providing more accurate and reliable data on pedestrian behavior.