Spatio Temporal Action
Spatio-temporal action localization aims to identify and precisely locate actions within videos, specifying both their spatial extent (bounding boxes) and temporal duration. Current research heavily utilizes deep learning, focusing on transformer-based architectures and efficient two-stream or single-stream networks that integrate spatial and temporal information, often incorporating techniques like keypoint detection and tracking to improve accuracy and speed. This field is crucial for advancing video understanding and has significant implications for applications such as robotics, security, and healthcare, where accurate and real-time action recognition is essential. Improvements in model efficiency are a key focus, enabling real-time performance for practical deployment.