TY - JOUR
AU - Rashid, Md. Harun -Ar-
AB - Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol. 8, No. 4, December 2020, pp. 757~769 ISSN: 2089-3272, DOI: 10.11591/ijeei.v8i4.2585  757 Student Activity Detection Using Deep Learning with YOLOv3 1 2 3 Md. Yousuf Ali , Xuan-De Zhang , Md. Harun-Ar-Rashid 1,2 Department of Computer Science and Technology, Shaanxi University of Science and Technology, Xian, China Department of Computer Science and Engineering, MawlanaBhashani Science and Technology University, Tangail, Bangladesh Article Info ABSTRACT This article describes the main phases of a new learning system by YOLOv3 Article history: is used for deep learning to identify student activities. Any unwanted problems Received Jul 2, 2020 in SUST- (Shaanxi University of Science and Technology) can be Revised Dec 2, 2020 circumvented by using this process. In this article, we have investigated the Accepted Dec 15, 2020 problem of image-based student activity detection in SUST. It involves making a prediction by analyzing student poses, behavior, and activities with objects from complex images instead of videos. Comparing with all Keyword: approaches, we conclusively decided to use an algorithm YOLOv3 (You Only Look Once) which is the latest and more convenient. The algorithm utilizes YOLOV3, anchor boxes, bounding boxes, and a variant of Darknet. 
TI - Student activities detection of SUST using YOLOv3 on Deep                        Learning
JF - Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
DO - 10.11591/ijeei.v8i4.2585
DA - 2020-12-30
UR - https://www.deepdyve.com/lp/unpaywall/student-activities-detection-of-sust-using-yolov3-on-deep-learning-0LlouS04rq
DP - DeepDyve
ER -