Driver Fatigue Detection Method Based on Human Pose Information Entropy
Driver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. However, this approach is affected by facial occlusions (glasses, sunglasses, masks, etc.) and the large fa...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/7213841 |
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| _version_ | 1832565823554715648 |
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| author | Taiguo Li Tiance Zhang Yingzhi Zhang Liben Yang |
| author_facet | Taiguo Li Tiance Zhang Yingzhi Zhang Liben Yang |
| author_sort | Taiguo Li |
| collection | DOAJ |
| description | Driver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. However, this approach is affected by facial occlusions (glasses, sunglasses, masks, etc.) and the large facial pose deformations in the extraction of effective fatigue features. In this paper, we introduce a novel DFD method using human pose information entropy. The method first estimates human pose from video sequences and then uses them as clues to extract multiple fatigue-related features which can reduce the influence of facial occlusion and head pose deformation. Information entropy and sliding window algorithm are applied to analyse and calculate sufficient consecutive video frames to obtain more robust and accurate fatigue-related values than by using a single frame. These information entropy values are combined resorting to the support vector machine (SVM) to recognize the driver fatigue state. Experimental results show that the method can achieve much higher accuracy and robustness, and the detection speed meets the requirements of real time. |
| format | Article |
| id | doaj-art-ecc7cc2966654b318cbd627dcd8eff0e |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-ecc7cc2966654b318cbd627dcd8eff0e2025-02-03T01:06:36ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/7213841Driver Fatigue Detection Method Based on Human Pose Information EntropyTaiguo Li0Tiance Zhang1Yingzhi Zhang2Liben Yang3School of Automation & Electrical EngineeringSchool of Automation & Electrical EngineeringSchool of Automation & Electrical EngineeringSchool of Automation & Electrical EngineeringDriver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. However, this approach is affected by facial occlusions (glasses, sunglasses, masks, etc.) and the large facial pose deformations in the extraction of effective fatigue features. In this paper, we introduce a novel DFD method using human pose information entropy. The method first estimates human pose from video sequences and then uses them as clues to extract multiple fatigue-related features which can reduce the influence of facial occlusion and head pose deformation. Information entropy and sliding window algorithm are applied to analyse and calculate sufficient consecutive video frames to obtain more robust and accurate fatigue-related values than by using a single frame. These information entropy values are combined resorting to the support vector machine (SVM) to recognize the driver fatigue state. Experimental results show that the method can achieve much higher accuracy and robustness, and the detection speed meets the requirements of real time.http://dx.doi.org/10.1155/2022/7213841 |
| spellingShingle | Taiguo Li Tiance Zhang Yingzhi Zhang Liben Yang Driver Fatigue Detection Method Based on Human Pose Information Entropy Journal of Advanced Transportation |
| title | Driver Fatigue Detection Method Based on Human Pose Information Entropy |
| title_full | Driver Fatigue Detection Method Based on Human Pose Information Entropy |
| title_fullStr | Driver Fatigue Detection Method Based on Human Pose Information Entropy |
| title_full_unstemmed | Driver Fatigue Detection Method Based on Human Pose Information Entropy |
| title_short | Driver Fatigue Detection Method Based on Human Pose Information Entropy |
| title_sort | driver fatigue detection method based on human pose information entropy |
| url | http://dx.doi.org/10.1155/2022/7213841 |
| work_keys_str_mv | AT taiguoli driverfatiguedetectionmethodbasedonhumanposeinformationentropy AT tiancezhang driverfatiguedetectionmethodbasedonhumanposeinformationentropy AT yingzhizhang driverfatiguedetectionmethodbasedonhumanposeinformationentropy AT libenyang driverfatiguedetectionmethodbasedonhumanposeinformationentropy |