Detecting group interactions by online association of trajectory data

Fan Chen, Andrea Cavallaro

Abstract: We propose a method for detecting group interactions for groups of varying number of objects. We model each object as a moving agent with a direction-aware interest map and group interactions as mutual interests between objects. After grouping objects into unit interactions individually in each frame, we solve the temporal association problem by tracking group interaction over consecutive frames. Optimal grouping is obtained by finding the maximum weight spanning tree of a directed graph formed by objects and their potential interactions. Experimental results show that our method obtained around 80% recalling rates on two publicly available datasets.