TY - GEN
T1 - Attention estimation for input switch in scalable multi-display environments
AU - Bu, Xingyuan
AU - Pei, Mingtao
AU - Jia, Yunde
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Multi-Display Environments (MDEs) have become commonplace in office desks for editing and displaying different tasks, such as coding, searching, reading, and video-communicating. In this paper, we present a method of automatic switch for routing one input (including mouse/keyboard, touch pad, joystick, etc.) to different displays in scalable MDEs based on the user attention estimation. We set up an MDE in our office desk, in which each display is equipped with a webcam to capture the user’s face video for detecting if the user is looking at the display. We use Convolutional Neural Networks (CNNs) to learn the attention model from face videos with various poses, illuminations, and occlusions for achieving a high performance of attention estimation. Qualitative and quantitative experiments demonstrate the effectiveness and potential of the proposed approach. The results of the user study also shows that the participants deemed that the system is wonderful, useful, and friendly.
AB - Multi-Display Environments (MDEs) have become commonplace in office desks for editing and displaying different tasks, such as coding, searching, reading, and video-communicating. In this paper, we present a method of automatic switch for routing one input (including mouse/keyboard, touch pad, joystick, etc.) to different displays in scalable MDEs based on the user attention estimation. We set up an MDE in our office desk, in which each display is equipped with a webcam to capture the user’s face video for detecting if the user is looking at the display. We use Convolutional Neural Networks (CNNs) to learn the attention model from face videos with various poses, illuminations, and occlusions for achieving a high performance of attention estimation. Qualitative and quantitative experiments demonstrate the effectiveness and potential of the proposed approach. The results of the user study also shows that the participants deemed that the system is wonderful, useful, and friendly.
KW - Attention estimation
KW - Convolutional neural network
KW - Input switch
KW - Multi-display environment
UR - http://www.scopus.com/inward/record.url?scp=84992718276&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46681-1_40
DO - 10.1007/978-3-319-46681-1_40
M3 - Conference contribution
AN - SCOPUS:84992718276
SN - 9783319466804
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 329
EP - 336
BT - Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
A2 - Ikeda, Kazushi
A2 - Lee, Minho
A2 - Hirose, Akira
A2 - Ozawa, Seiichi
A2 - Doya, Kenji
A2 - Liu, Derong
PB - Springer Verlag
T2 - 23rd International Conference on Neural Information Processing, ICONIP 2016
Y2 - 16 October 2016 through 21 October 2016
ER -