Recent Studies on Motion Detection for Education Environment using Computer Vision with Deep Architecture
DOI:
https://doi.org/10.11113/ijic.v16n1-2.685Keywords:
Motion Detection, Deep Learning, Computer Vision, Student Environment, Classroom MonitoringAbstract
The integration of computer vision and deep learning within educational environments offers innovative methods for enhancing student engagement and focus assessment. Traditional classroom observation is often limited by time constraints and subjective biases, creating a need for objective and automated monitoring solutions. Recent advancements in AI-based motion detection, particularly using deep learning architectures such as CNNs, YOLO variants, R-FCN, and LSTMs, show promising results in tracking and analyzing student behaviors and engagement through movement and positioning. This review explores state-of-the-art models used in educational settings to classify and detect behaviors, emotional states, and engagement levels based on observable cues, including facial expressions, gestures, and posture. Furthermore, optimizing camera placement and using multi-angle configurations are identified as crucial factors for improving detection accuracy in complex classroom setups. Experimental findings on model performance, including precision, box loss, and classification accuracy, reveal these systems’ efficacy and limitations, with some challenges persisting in precise localization. Despite these, the practical implications of real-time motion detection systems are significant, enabling educators to create responsive and interactive learning environments. This study underscores the potential of AI-enhanced monitoring in transforming classroom dynamics and promoting an engaging educational experience.
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