Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning

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dc.authoridCELIK, YUKSEL/0000-0002-7117-9736
dc.contributor.authorSalim, Laith Mohammed
dc.contributor.authorCelik, Yuksel
dc.date.accessioned2024-09-29T16:08:05Z
dc.date.available2024-09-29T16:08:05Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractDangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene.en_US
dc.identifier.doi10.3390/electronics13112116
dc.identifier.issn2079-9292
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85195869023en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/electronics13112116
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7350
dc.identifier.volume13en_US
dc.identifier.wosWOS:001246700300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofElectronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjecthuman behavior recognitionen_US
dc.subjecthuman activity recognitionen_US
dc.subjectoptical flowen_US
dc.subjectdeep learningen_US
dc.subjectstacked autoencoderen_US
dc.titleDetection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learningen_US
dc.typeArticleen_US

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