Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines
المؤلفون المشاركون
Yousaf, Muhammad Haroon
Irtaza, Aun
Nida, Nudrat
Velastin, Sergio A.
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-04-30
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Human action recognition has the potential to predict the activities of an instructor within the lecture room.
Evaluation of lecture delivery can help teachers analyze shortcomings and plan lectures more effectively.
However, manual or peer evaluation is time-consuming, tedious and sometimes it is difficult to remember all the details of the lecture.
Therefore, automation of lecture delivery evaluation significantly improves teaching style.
In this paper, we propose a feedforward learning model for instructor’s activity recognition in the lecture room.
The proposed scheme represents a video sequence in the form of a single frame to capture the motion profile of the instructor by observing the spatiotemporal relation within the video frames.
First, we segment the instructor silhouettes from input videos using graph-cut segmentation and generate a motion profile.
These motion profiles are centered by obtaining the largest connected components and normalized.
Then, these motion profiles are represented in the form of feature maps by a deep convolutional neural network.
Then, an extreme learning machine (ELM) classifier is trained over the obtained feature representations to recognize eight different activities of the instructor within the classroom.
For the evaluation of the proposed method, we created an instructor activity video (IAVID-1) dataset and compared our method against different state-of-the-art activity recognition methods.
Furthermore, two standard datasets, MuHAVI and IXMAS, were also considered for the evaluation of the proposed scheme.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Nida, Nudrat& Yousaf, Muhammad Haroon& Irtaza, Aun& Velastin, Sergio A.. 2019. Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1194778
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Nida, Nudrat…[et al.]. Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1194778
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Nida, Nudrat& Yousaf, Muhammad Haroon& Irtaza, Aun& Velastin, Sergio A.. Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1194778
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1194778
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر