Crowd gathering and commotion detection based on the stillness and motion model

The abnormal event detection becomes an important topic recently. This paper presents a method to detect the crowd gathering, as well as the commotion event after the crowd gathering. The proposed stillness model and the motion model are based on the improved background subtraction and the optical f...

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Published inMultimedia tools and applications Vol. 79; no. 27-28; pp. 19435 - 19449
Main Authors Yang, Deng-Shun, Liu, Chun-Yu, Liao, Wei-Hao, Ruan, Shanq-Jang
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2020
Springer Nature B.V
Subjects
Behavior
Cameras
Clustering
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Experiments
Multimedia
Multimedia Information Systems
Neural networks
Optical flow (image analysis)
Special Purpose and Application-Based Systems
Subtraction
Surveillance
Crowd abnormal behaviors
Image recognition
Crowd feature engineering
Surveillance application
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Summary:The abnormal event detection becomes an important topic recently. This paper presents a method to detect the crowd gathering, as well as the commotion event after the crowd gathering. The proposed stillness model and the motion model are based on the improved background subtraction and the optical flow feature. We construct the long-term stillness level by the break bucket model and clustering the instantaneous stillness level. Then the crowd gathering event is decided by the threshold with the long-term stillness level. Furthermore, the motion model is applied for detecting the commotion event after the crowd gathering. In the experiment, we used the dataset of PET2009. The proposed method is verified by the experiment with 97% accuracy.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-08827-4