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Social Group Discovery from Surveillance Videos: A Data-Driven Approach with Attention-Based Cues

This paper presents an approach to discover social groups in surveillance videos by incorporating attention-based cues to model group behaviors of pedestrians in videos. Group behaviors are modeled as a set of decision trees with the decisions being basic measurements based on positionbased and attention-based cues. Rather than enforcing explicit models, we apply tree-based learning algorithms to implicitly construct the decision tree models. The experimental results demonstrate that incorporating attention-based cues significantly increased the estimation accuracy compared to the conventional approaches that used position-based cues alone.