A Heat-Map-based Algorithm for Recognizing Group Activities in Videos
This work addresses activity recognition for video analysis, but it appears incremental as it builds on existing methods with new features.
The paper tackles group activity recognition in videos by proposing a heat-map-based algorithm that models human trajectories as heat sources and uses thermal diffusion, achieving effectiveness in experiments.
In this paper, a new heat-map-based (HMB) algorithm is proposed for group activity recognition. The proposed algorithm first models human trajectories as series of "heat sources" and then applies a thermal diffusion process to create a heat map (HM) for representing the group activities. Based on this heat map, a new key-point based (KPB) method is used for handling the alignments among heat maps with different scales and rotations. And a surface-fitting (SF) method is also proposed for recognizing group activities. Our proposed HM feature can efficiently embed the temporal motion information of the group activities while the proposed KPB and SF methods can effectively utilize the characteristics of the heat map for activity recognition. Experimental results demonstrate the effectiveness of our proposed algorithms.