Abnormal Driving Behavior Detection Using Sparse Representation

Chien Yu Chiou, Pau-Choo Chung, Chun Rong Huang, Ming Fang Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 2016 International Computer Symposium, ICS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages390-395
Number of pages6
ISBN (Electronic)9781509034383
DOIs
Publication statusPublished - 2017 Feb 16
Event2016 International Computer Symposium, ICS 2016 - Chiayi, Taiwan
Duration: 2016 Dec 152016 Dec 17

Publication series

NameProceedings - 2016 International Computer Symposium, ICS 2016

Other

Other2016 International Computer Symposium, ICS 2016
CountryTaiwan
CityChiayi
Period16-12-1516-12-17

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Monitoring

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Chiou, C. Y., Chung, P-C., Huang, C. R., & Chang, M. F. (2017). Abnormal Driving Behavior Detection Using Sparse Representation. In Proceedings - 2016 International Computer Symposium, ICS 2016 (pp. 390-395). [7858507] (Proceedings - 2016 International Computer Symposium, ICS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICS.2016.0085
Chiou, Chien Yu ; Chung, Pau-Choo ; Huang, Chun Rong ; Chang, Ming Fang. / Abnormal Driving Behavior Detection Using Sparse Representation. Proceedings - 2016 International Computer Symposium, ICS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 390-395 (Proceedings - 2016 International Computer Symposium, ICS 2016).
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Chiou, CY, Chung, P-C, Huang, CR & Chang, MF 2017, Abnormal Driving Behavior Detection Using Sparse Representation. in Proceedings - 2016 International Computer Symposium, ICS 2016., 7858507, Proceedings - 2016 International Computer Symposium, ICS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 390-395, 2016 International Computer Symposium, ICS 2016, Chiayi, Taiwan, 16-12-15. https://doi.org/10.1109/ICS.2016.0085

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 proceedingConference contribution

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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.

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Chiou CY, Chung P-C, Huang CR, Chang MF. Abnormal Driving Behavior Detection Using Sparse Representation. In 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). https://doi.org/10.1109/ICS.2016.0085