Environmental and social life cycle assessment to enhance sustainability of sugarcane-based products in Thailand

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Prasara-A J., Gheewala S.H., Silalertruksa T., Pongpat P., Sawaengsak W.

ผู้เผยแพร่: Society of Photo-optical Instrumentation Engineers

ปีที่เผยแพร่ (ค.ศ.): 2019

Volume number: 21

Issue number: 7

หน้าแรก: 1447

หน้าสุดท้าย: 1458

จำนวนหน้า: 12

ISBN: 9781510627734

นอก: 0277-786X

eISSN: 1996-756X

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

ภาษา: English-Great Britain (EN-GB)

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บทคัดย่อ

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.

คำสำคัญ

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