Detection of social groups in pedestrian crowds using computer vision

We present a novel approach for automatic detection of social groups of pedestrians in crowds. Instead of computing pairwise similarity between pedestrian trajectories, followed by clustering of similar pedestrian trajectories into groups, we cluster pedestrians into a groups by considering only start (source) and stop (sink) locations of their trajectories. The paper presents the proposed approach and its evaluation using different datasets: experimental results demonstrate its effectiveness achieving significant accuracy both under dichotomous and trichotomous coding schemes. Experimental results also show that our approach is less computationally expensive than the current state-of-the-art methods.