An improved fatigue detection system based on behavioral characteristics of driver

Gupta, Rajat; Aman, Kanishk; Shiva, Nalin; Singh, Yadvendra

doi:10.1109/icite.2017.8056914

Cited by 22 publications

(3 citation statements)

References 16 publications

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“…Zhang et al created a model to solve the influence of the sunglasses on the fatigue detection, which used the IRF dataset [9]. Gupta et al observed the facial features of the driver through a camera and classified the fatigue levels through principal component analysis and support vector machine (SVM) classifier [10]. Junaedi and Akbar calculated PERCLOS by detecting the eyes and used it to judge the fatigue.…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

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In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.

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Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

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“…They focused only on the eye and mouth regions and ignored the rest. By concentrating more on the eye and mouth, they reduced unwanted characteristics in the feature set [7]. In paper [8], a model for facial feature recognition was proposed.…”

Section: Related Workmentioning

confidence: 99%

Driver’s Drowsiness Detection System Using Dlib HOG

Babu

Nair

Sreekumar

2021

Smart Innovation, Systems and Technologies

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For human beings, sleep is a key requirement. The secret of humankind's physical well-being is sleep. In a study on sleep, researchers have proved that adults from the age of eighteen and above must get seven to nine hours of sleep a day. Drowsiness is the root cause of the hazardous road accidents. If drivers are notified as drowsy at the correct instant of time, we can prevent the majority of road accidents that took place in the world. New strategies are introduced by the researchers to detect the drowsiness of the driver and each technology has its own merit and demerit. This paper uses Python and Dlib models to build a drowsiness identification model. We aim to integrate both face detection and head pose detection which makes this an ideal detection method. In the proposed system, a laptop is used, using which real-time video is recorded. Head-pose detection along with face detection helps to increase accuracy. For dataset video input, the proposed system gives a maximum accuracy rate of 94.51%.

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“…In the literature [7] , the haar feature is used to recognize the face, the eye position is determined by the geometric relationship of the facial organs, the UCK filter algorithm is used to track the human eye movement, and finally the PERCLOS fatigue judgment index is used to judge the fatigue degree; the literature [8] First analyze the driver's facial features such as: eyes (fast blinking or heavy eyes) and mouth (yawn detection), and then use support vector machine to analyze the fatigue level of its parameter processing; literature [9][10][11][12][13][14][15] is mostly similar to the above content , are based on the driver's face information detected by the optimized traditional visual processing algorithm, and the eye and mouth position information is obtained to judge the driver's fatigue state. With the in-depth development of visual technology, the deep learning algorithm that can extract more information has gradually replaced the traditional visual algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization of extreme position landmarks identification algorithm for driver fatigue state

MA,

Qin,

Qin

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Aiming at the problem of poor fitting effect of key points of the driver's mouth and eyes during driver fatigue monitoring, a convolutional neural network PFLD-rep based on improved PFLD was proposed. First, the backbone network is structurally re-parameterized, which can not only retain multi-scale parameters but also achieve the calculation speed of a single branch; then introduce the idea of heat map regression to the auxiliary network to improve the spatial expression ability of the network in the mouth and improve fitting. Effect; Add the opening angle of the eyes and mouth to the loss function, and replace the original L2_loss with Wing_loss loss to increase the network's attention to mouth deformation; later, a high-precision comprehensive fatigue judgment criterion will be determined through experiments. The final experimental results show that the IPN value of the PFLD-rep algorithm in the 300W data set reaches 3.68. The algorithm also has a high fitting effect in faces with large deformations, and can be implemented when vehicle-mounted hardware equipment requirements are low. Higher precision monitoring effect.

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An improved fatigue detection system based on behavioral characteristics of driver

Cited by 22 publications

References 16 publications

Driver Fatigue Detection Based on Facial Key Points and LSTM

Driver Fatigue Detection Based on Facial Key Points and LSTM

Driver’s Drowsiness Detection System Using Dlib HOG

Optimization of extreme position landmarks identification algorithm for driver fatigue state

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