In this paper, we address the problem of recognizing group activities of human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene to effectively handle noisy information. Two novel features, Group Interaction Energy (GIE) feature and Attraction and Repulsion Features, are proposed to better describe group activities within a GIZ. We demonstrate the performance of our method in two ways by (i) comparing the performance of the proposed method with the previous methods and (ii) analyzing the influence of the proposed features and GIZ-based meaningful group detection on group activity recognition using public datasets.
|Journal||International Journal of Pattern Recognition and Artificial Intelligence|
|Publication status||Published - 2015 Aug 11|
Bibliographical noteFunding Information:
The research was supported by the Implementation of Technologies for Identi¯cation, Behavior, and Location of Human based on Sensor Network Fusion Program through the Ministry of Trade, Industry and Energy (Grant No. 10041629) and the 2014 R&D Program for S/W Computing Industrial Core Technology through the MSIP (Ministry of Science, ICT and Future Planning)/KEIT (Korea Evaluation Institute of Industrial Technology) (Project No. 14-824-09-005).
© 2015 World Scientific Publishing Company.
- Human group activity recognition
- machine vision
- pattern recognition
- visual surveillance
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence