Environmentally friendly interlocking concrete paving block containing new cementing material and recycled concrete aggregate

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Publication Details

Author list: Abdulmatin A., Tangchirapat W., Jaturapitakkul C.

Publisher: Society of Photo-optical Instrumentation Engineers

Publication year: 2019

Volume number: 23

Issue number: 12

Start page: 1467

End page: 1484

Number of pages: 18

ISBN: 9781510627734

ISSN: 0277-786X

eISSN: 1996-756X

URL: https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85063871327&doi=10.1117%2f12.2521657&partnerID=40&md5=426028a7a22ec968235684859f853912

Languages: English-Great Britain (EN-GB)

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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. ฉ COPYRIGHT SPIE.

Keywords

Activity Classication, Activity Monitoring, Gait Energy Image (GEI), SVM