Group Detection

Group detection in visual data aims to automatically identify clusters of individuals engaged in shared activities, a crucial task with applications in public safety and social interaction analysis. Recent research emphasizes the use of deep learning models, particularly graph neural networks and recurrent neural networks like LSTMs, to leverage both spatial and temporal information from video data, including human pose, movement, and gaze. Addressing challenges like occlusions in large-scale scenes and the need for large, realistic datasets is driving the development of novel architectures and simulation techniques. Improved group detection algorithms promise advancements in understanding social dynamics and enhancing automated surveillance systems.