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

Natticha Nichaweerasit; Watcharapan Suwansantisuk; Pinit Kumhom

doi:10.1117/12.2521657

22 March 2019 Classification of student activities based on a sequence of images from a single camera

Natticha Nichaweerasit, Watcharapan Suwansantisuk, Pinit Kumhom

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110491N (2019) https://doi.org/10.1117/12.2521657
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

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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Natticha Nichaweerasit, Watcharapan Suwansantisuk, and Pinit Kumhom "Classification of student activities based on a sequence of images from a single camera", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110491N (22 March 2019); https://doi.org/10.1117/12.2521657

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KEYWORDS

Gait analysis

Cameras

Principal component analysis

Skin

Facial recognition systems

Image classification

Video

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