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This content will become publicly available on October 31, 2022

Title: Talking Detection in Collaborative Learning Environments.
We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves.
Authors:
; ; ;
Award ID(s):
1842220 1613637 1949230
Publication Date:
NSF-PAR ID:
10310070
Journal Name:
Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science.
Volume:
13053
Sponsoring Org:
National Science Foundation
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