A video-based DT–SVM school violence detecting algorithm
|Author:||Ye, Liang1,2; Wang, Le1,3; Ferdinando, Hany2,4;|
1Department of Information and Communication Engineering, Harbin Institute of Technology, Harbin150080, China
2OPEM Research Unit, University of Oulu, 90014 Oulu, Finland
3Huawei Beijing Institute, Beijing 100085, China
4Department of Electrical Engineering, Petra Christian University, Surabaya 60236, Indonesia
5Physiological Signal Analysis Team, University of Oulu, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020070346874
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2020-07-03
School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This paper proposes a video-based school violence detecting algorithm. This algorithm first detects foreground moving targets via the KNN (K-Nearest Neighbor) method and then preprocesses the detected targets via morphological processing methods. Then, this paper proposes a circumscribed rectangular frame integrating method to optimize the circumscribed rectangular frame of moving targets. Rectangular frame features and optical-flow features were extracted to describe the differences between school violence and daily-life activities. We used the Relief-F and Wrapper algorithms to reduce the feature dimension. SVM (Support Vector Machine) was applied as the classifier, and 5-fold cross validation was performed. The accuracy was 89.6%, and the precision was 94.4%. To further improve the recognition performance, we developed a DT–SVM (Decision Tree–SVM) two-layer classifier. We used boxplots to determine some features of the DT layer that are able to distinguish between typical physical violence and daily-life activities and between typical daily-life activities and physical violence. For the remainder of activities, the SVM layer performed a classification. For this DT–SVM classifier, the accuracy reached 97.6%, and the precision reached 97.2%, thus showing a significant improvement.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
217 Medical engineering
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
This research was funded by National Natural Science Foundation of China, grant number 41861134010; Basic scientific research project of Heilongjiang Province, grant number KJCXZD201704; Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security, grant number 2018JYWXTX01, and partly by Finnish Cultural Foundation, North-Ostrobothnia Regional Fund 2017.
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