Video Detection

Video detection research focuses on automatically identifying and classifying objects, actions, and events within video sequences, aiming to improve accuracy, efficiency, and robustness across diverse applications. Current efforts concentrate on developing advanced deep learning models, including transformers and convolutional neural networks, often incorporating multi-modal data fusion (e.g., combining LiDAR and camera data) and temporal information processing to enhance performance. These advancements have significant implications for various fields, from improving autonomous driving safety and facilitating medical diagnoses (e.g., polyp detection) to enhancing video security and understanding human behavior in different contexts.