Ms. Natticha Nichaweerasit Profile

Natticha Nichaweerasit

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Proceedings Article | 22 March 2019 Paper

Classification of student activities based on a sequence of images from a single camera

Natticha Nichaweerasit, Watcharapan Suwansantisuk, Pinit Kumhom

Proceedings Volume 11049, 110491N (2019) https://doi.org/10.1117/12.2521657

KEYWORDS: Gait analysis, Cameras, Principal component analysis, Skin, Image classification, Facial recognition systems, Video, Target detection, Scanning transmission electron microscopy, Mathematics

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In an active learning environment, a student activities is crucial to his/her learning achievment. However, keeping track of the student activities by teaching staffs is almost impossible. Hence, using technology for such tedious but important job has become attractive or even necessary. Focusing on such environment, this paper proposes a method of classifying whether a student is writing or reading or working on other things such as doing experiments based on sequential image frames from a single camera. For each frame, an area including the student is cropped out using a background subtraction and thresholding. Then, using the skin detection technique, face and hands of the target students are detected. Such face and hand areas of n sequence of frames are combined as a Gait Energy Image (GEI), which is being used as feature images for the classification in which the Principal Component Analysis is applied. A sum score of the PCA in which each row as an observed sample is taken as a feature while another score of PCA in which a column is considered to be an observed sample is taken as another feature. Using the support vector machine, the two features are used to classify whether a student is “reading” or “not reading” first. Then, for a “not reading” sample, it is classified whether it is “writing” or “doing experiment”. Based-on a sequence of simulated activities, the proposed method can classfy between “reading” and “not reading” with 93% accuracy while the classifying between “writing” and “doing experiment” class achieved 90% accuracy.

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