Abstract
Information-based classrooms with cameras provide numerous videos for evaluating teaching qualities. In this paper, we focus on automatically detecting the learning-and-teaching related behaviors for further teaching analysis, including standing-up and hand-raising of students, and movements of teachers. First, due to the continuity of behaviors, we convert it into a frame-based object detection problem. Compared with the publicly-available datasets of object detection, there are several challenges in our real classrooms, such as low resolutions, various gestures, complex backgrounds, and occlusions. Second, to solve these challenges, we propose an improved R-FCN architecture, which incorporates Feature Pyramid Networks into Region Proposal Networks (RPNs) and introduces a position-sensitive RoIAlign layer. The multi-level features and RPNs provide more contexture information for small object detections, and the position-sensitive RoIAlign layer reduces the misalignment in extracting features for region proposals. Lastly, the efficiency of the proposed algorithm is demonstrated on our collected data from real classrooms, which contains 30k frames with 100k labeled objects.
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Acknowledgements
The research was supported by NSFC (No. 61671290), the Key Program for International S&T Cooperation Project of China (No. 2016YFE0129500), and Shanghai Committee of Science and Technology (No. 17511101903).
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Shao, B., Jiang, F., Shen, R. (2018). Multi-object Detection Based on Deep Learning in Real Classrooms. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_40
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