Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines
Joint Authors
Yousaf, Muhammad Haroon
Irtaza, Aun
Nida, Nudrat
Velastin, Sergio A.
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194778