Abstract
This research proposes a motion recognition system for early detection of students' physical aggressive behavior in the classroom. The motion recognition system recognizes physical attacks so that teachers can resolve disputes early to reduce other greater injuries. In the beginning, cameras were used in this system to monitor students’ classroom activities and to obtain body images by removing background and saliency maps. Two angles from arm to shoulder and shoulder to the center of the body are then measured and the velocity between the two frames from the movement of the body is detected, and use these angle and velocity values as the criterion for judging whether it is a physical attack. In the end, the accuracy of the proposed algorithms is verified by using the confusion matrix based on machine learning and the minimum cross entropy based on neural networks. The simulation proves that the proposed algorithm can correctly detect the attack behavior of the collected videos.
Highlights
A visual attention system, confusion matrix, and cross entropy minimization are introduced as follows
“Visual attention system” presents the visual attention system, “Confusion matrix” introduces the confusion matrix, and “Cross entropy minimization” statements the cross entropy minimization used in this work
To make this motion recognition system have the purpose of self-upgrading, the method of improving the accuracy in this paper is to minimize the cross-entropy and adjust the threshold of the region of interest (ROI) movement velocity
Summary
A visual attention system, confusion matrix, and cross entropy minimization are introduced as follows. The proposed system initially removes the background of the image and applies a saliency map scheme to extract the ROI part of the body. The training phase contains only one part, that is, by minimizing the cross-entropy and confusion matrix to compute the accuracy of the offensive behavior obtained by the candidate in the testing phase.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
