A Heat-Map-based Algorithm for Recognizing Group Activities in Videos

TL;DR

This paper introduces a heat-map-based algorithm that models human trajectories as heat sources and employs thermal diffusion, key-point alignment, and surface fitting to recognize group activities in videos effectively.

Contribution

It presents a novel heat-map-based approach with key-point and surface-fitting methods for improved group activity recognition in videos.

Findings

Effective embedding of temporal motion information

Accurate recognition of group activities

Demonstrated superior performance in experiments

Abstract

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.