A Video-Based DT–SVM School Violence Detecting Algorithm

Abstract

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.

Moment Features based Violence Action Detection using Optical Flow

“…Although, they performed feature extraction using both space and time dimensions to create spatio-temporal features through a custom build convolutional neural network, estimation of processing time per frame was ignored in their research. Research in [22] received accuracy rate of 97.6% by designing DT-SVM (Decision Tree SVM) using prior determined features to distinguish physical violence from daily-life activities. However, in case of complex scenes with both nearby and distant objects, their research could not provide satisfactory validation results.…”

“…Although, they created spatio-temporal features by performing features extraction using both space and time dimensions through a custom build convolutional neural network and long short term memory LSTM recurrent neural network, validation against computation time or processing time per frame was ignored in their research. Research in [22] selected some prior features to distinguish physical violence from daily-life activities. They designed DT-SVM (Decision Tree-SVM) two-layer classifier, i.e.…”

“…Besides, motion pattern based on moment features extraction is used to reduce background complexity in case of similar colored objects by implicating weighted average of pictorial intensities. Optical flow energy characteristics [28] Oriented VIolent Flows (OViF) [27] CNN with long short-term memory (CNN-LSTM) [20] Local Binary Pattern (LBP) [24] Bag-of-Words framework [25] Grey Level Cooccurrence Matrix (GLCM) [23] Deep Neural Network [21] Decision Tree-SVM (DT-SVM) [22] Acceleration Measure Vectors (AMV) [26] www.ijacsa.thesai.org…”

“…Various performance metrics are used to validate the proposed methodology, i.e. accuracy rate [22,35,36], error [27,37,38], computation time [28,39,40] and frame rate [20,41,42,43,44].…”

“…Research in [21] used set of selectively distributed frames and spatio-temporal features using both space and time dimensions provided to fully connected neural framework to classify violence or nonviolence action which required further validation in terms with complexity. Research in [22] distinguished physical violence by designing DT-SVM (Decision Tree-SVM) two-layer classifier. However, their overall methodology requires further improvement towards more complex scenes with both nearby and distant objects.…”