Abstract
To reduce the chance of traffic crashes, many driver monitoring systems (DMSs) have been developed. A DMS warns the driver under abnormal driving conditions. However, traditional approaches require enumerating abnormal driving conditions. In this paper, we propose a novel DMS, which models the driver's normal driving statuses based on sparse reconstruction. The proposed DMS compares the driver's statuses with his/her personal normal driving status model and identifies abnormal driving statuses that greatly change the driver's appearances. The experimental results show good performance of the proposed DMS to detect variant abnormal driver conditions.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 International Computer Symposium, ICS 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 390-395 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509034383 |
| DOIs | |
| Publication status | Published - 2017 Feb 16 |
| Event | 2016 International Computer Symposium, ICS 2016 - Chiayi, Taiwan Duration: 2016 Dec 15 → 2016 Dec 17 |
Publication series
| Name | Proceedings - 2016 International Computer Symposium, ICS 2016 |
|---|
Other
| Other | 2016 International Computer Symposium, ICS 2016 |
|---|---|
| Country | Taiwan |
| City | Chiayi |
| Period | 16-12-15 → 16-12-17 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Computer Networks and Communications
- Computer Science Applications
Cite this
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Abnormal Driving Behavior Detection Using Sparse Representation. / Chiou, Chien Yu; Chung, Pau-Choo; Huang, Chun Rong; Chang, Ming Fang.
Proceedings - 2016 International Computer Symposium, ICS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 390-395 7858507 (Proceedings - 2016 International Computer Symposium, ICS 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Abnormal Driving Behavior Detection Using Sparse Representation
AU - Chiou, Chien Yu
AU - Chung, Pau-Choo
AU - Huang, Chun Rong
AU - Chang, Ming Fang
PY - 2017/2/16
Y1 - 2017/2/16
N2 - To reduce the chance of traffic crashes, many driver monitoring systems (DMSs) have been developed. A DMS warns the driver under abnormal driving conditions. However, traditional approaches require enumerating abnormal driving conditions. In this paper, we propose a novel DMS, which models the driver's normal driving statuses based on sparse reconstruction. The proposed DMS compares the driver's statuses with his/her personal normal driving status model and identifies abnormal driving statuses that greatly change the driver's appearances. The experimental results show good performance of the proposed DMS to detect variant abnormal driver conditions.
AB - To reduce the chance of traffic crashes, many driver monitoring systems (DMSs) have been developed. A DMS warns the driver under abnormal driving conditions. However, traditional approaches require enumerating abnormal driving conditions. In this paper, we propose a novel DMS, which models the driver's normal driving statuses based on sparse reconstruction. The proposed DMS compares the driver's statuses with his/her personal normal driving status model and identifies abnormal driving statuses that greatly change the driver's appearances. The experimental results show good performance of the proposed DMS to detect variant abnormal driver conditions.
UR - http://www.scopus.com/inward/record.url?scp=85015326370&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015326370&partnerID=8YFLogxK
U2 - 10.1109/ICS.2016.0085
DO - 10.1109/ICS.2016.0085
M3 - Conference contribution
T3 - Proceedings - 2016 International Computer Symposium, ICS 2016
SP - 390
EP - 395
BT - Proceedings - 2016 International Computer Symposium, ICS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -