Abstract
This paper proposes a design of an auto-aiming system, which is capable of tracking a user's gaze automatically. The auto-aiming system is composed of an electric gun control system and a wearable gaze tracker. According to a user's gaze direction which is captured by the helmet camera, the gun can automatically aim an object which the user is looking at. The mapping model between the user's eye movement and the shooting direction of the gun is established by a Deep Belief Network (DBN). This DBN-based gaze tracking model allows the auto-aiming shooting system to aim the target timely and accurately. Finally, the experiment results demonstrate the proposed auto-aiming system for different users can achieve an average accuracy of 96%.
| Original language | English |
|---|---|
| Title of host publication | 2017 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781538624197 |
| DOIs | |
| Publication status | Published - 2018 Feb 20 |
| Event | 2017 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2017 - Taipei, Taiwan Duration: 2017 Sep 6 → 2017 Sep 8 |
Publication series
| Name | International Conference on Advanced Robotics and Intelligent Systems, ARIS |
|---|---|
| Volume | 2017-September |
| ISSN (Print) | 2374-3255 |
| ISSN (Electronic) | 2572-6919 |
Other
| Other | 2017 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2017 |
|---|---|
| Country | Taiwan |
| City | Taipei |
| Period | 17-09-06 → 17-09-08 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Artificial Intelligence
Cite this
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Deep belief network based gaze tracker for auto-aiming system. / Chien, Chih Wei; Tsai, Ting Nan; Wu, L. F.; Fang, N. C.; Liu, C. Y.; Li, Tzuu Hseng S.
2017 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2017. Institute of Electrical and Electronics Engineers Inc., 2018. 8297183 (International Conference on Advanced Robotics and Intelligent Systems, ARIS; Vol. 2017-September).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Deep belief network based gaze tracker for auto-aiming system
AU - Chien, Chih Wei
AU - Tsai, Ting Nan
AU - Wu, L. F.
AU - Fang, N. C.
AU - Liu, C. Y.
AU - Li, Tzuu Hseng S.
PY - 2018/2/20
Y1 - 2018/2/20
N2 - This paper proposes a design of an auto-aiming system, which is capable of tracking a user's gaze automatically. The auto-aiming system is composed of an electric gun control system and a wearable gaze tracker. According to a user's gaze direction which is captured by the helmet camera, the gun can automatically aim an object which the user is looking at. The mapping model between the user's eye movement and the shooting direction of the gun is established by a Deep Belief Network (DBN). This DBN-based gaze tracking model allows the auto-aiming shooting system to aim the target timely and accurately. Finally, the experiment results demonstrate the proposed auto-aiming system for different users can achieve an average accuracy of 96%.
AB - This paper proposes a design of an auto-aiming system, which is capable of tracking a user's gaze automatically. The auto-aiming system is composed of an electric gun control system and a wearable gaze tracker. According to a user's gaze direction which is captured by the helmet camera, the gun can automatically aim an object which the user is looking at. The mapping model between the user's eye movement and the shooting direction of the gun is established by a Deep Belief Network (DBN). This DBN-based gaze tracking model allows the auto-aiming shooting system to aim the target timely and accurately. Finally, the experiment results demonstrate the proposed auto-aiming system for different users can achieve an average accuracy of 96%.
UR - http://www.scopus.com/inward/record.url?scp=85054335004&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054335004&partnerID=8YFLogxK
U2 - 10.1109/ARIS.2017.8297183
DO - 10.1109/ARIS.2017.8297183
M3 - Conference contribution
AN - SCOPUS:85054335004
SN - 9781538624197
T3 - International Conference on Advanced Robotics and Intelligent Systems, ARIS
BT - 2017 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -