Group Event Detection with a Varying Number of Group Members for Video Surveillance

TL;DR

This paper introduces a new method for recognizing group activities in video surveillance by using a group representative and an Asynchronous Hidden Markov Model to handle varying group sizes and complex interactions.

Contribution

It presents a novel approach combining a group representative and AHMM for flexible and hierarchical group activity detection in videos.

Findings

Effective detection of symmetric and asymmetric group activities

Handles varying group sizes with a group representative

Demonstrates hierarchical interaction detection

Abstract

This paper presents a novel approach for automatic recognition of group activities for video surveillance applications. We propose to use a group representative to handle the recognition with a varying number of group members, and use an Asynchronous Hidden Markov Model (AHMM) to model the relationship between people. Furthermore, we propose a group activity detection algorithm which can handle both symmetric and asymmetric group activities, and demonstrate that this approach enables the detection of hierarchical interactions between people. Experimental results show the effectiveness of our approach.