Relationship existence recognition-based social group detection in urban public spaces

and the temporal information to recognise the existence of social relationships through supervised deep learning. The latter estimates the similarities of trajectory pairs using the unsupervised spatial–temporal position information. Social group detection achieves superior accuracy with the two components’ complementary results. On Social-CAD (Social Collective Activity Dataset) and PLPS (Public Life in Public Space) datasets, extensive experiments demonstrate that our algorithm outperforms the state-of-the-art (SOTA) methods.