Multiple Scale Faster-RCNN Approach to Driver’s Cell-Phone Usage and Hands on Steering Wheel Detection

Le, Trung-Hieu; Zheng, Yuejiu; Zhu, Chenchen; Luu, Khoa; Savvides, Marios

doi:10.1109/cvprw.2016.13

Cited by 97 publications

(43 citation statements)

References 13 publications

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“…Results yielded a 95.56% accuracy in classification of the driver's mouth, ear and eye [18]. Le et al, 2016 [19], used an advanced deep learning approach that detects objects such as hands, cell-phone usage. Le et al, 2016, proposed deep learning technique features a Multiple Scale Faster-RCNN integrated with a standard Region Proposal Network(RPN) which features maps from that entails a convolution feature maps such as ROI pooling, conv4 and con3.…”

Section: B Distractions and Methodologies (Lstmetc)mentioning

confidence: 99%

Context-Aware Driver Distraction Severity Classification using LSTM Network

Fasanmade

Aliyu

et al. 2019

2019 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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Advanced Driving Assistance Systems (ADAS) has been a critical component in vehicles and vital to the safety of vehicle drivers and public road transportation systems. In this paper, we present a deep learning technique that classifies drivers' distraction behaviour using three contextual awareness parameters: speed, manoeuver and event type. Using a video coding taxonomy, we study drivers' distractions based on events information from Regions of Interest (RoI) such as hand gestures, facial orientation and eye gaze estimation. Furthermore, a novel probabilistic (Bayesian) model based on the Long shortterm memory (LSTM) network is developed for classifying driver's distraction severity. This paper also proposes the use of frame-based contextual data from the multi-view TeleFOT naturalistic driving study (NDS) data monitoring to classify the severity of driver distractions. Our proposed methodology entails recurrent deep neural network layers trained to predict driver distraction severity from time series data.

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Section: B Distractions and Methodologies (Lstmetc)mentioning

confidence: 99%

Context-Aware Driver Distraction Severity Classification using LSTM Network

Fasanmade

Aliyu

et al. 2019

2019 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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“…Their model operates face, mouth, and hand features of images obtained from a camera mounted above the dashboard. [24] devised a Faster-RCNN model to detect driver's cell-phone usage and "hands on the wheel". Their model is mainly geared towards face/hand segmentation.…”

Section: Cell Phone Usage Detectionmentioning

confidence: 99%

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

Eraqi

Abouelnaga

Saad

et al. 2019

Journal of Advanced Transportation

234

112

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The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.

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“…A great may of approaches have been raised for automatic driving behaviour recognition over the last decades or so. On the basis of feature used in their approaches, we can classify them into two categories of handcrafted feature based approaches [7,8,9,10] and deep learning based approaches [11,12,14].…”

Section: Related Workmentioning

confidence: 99%

“…In [12], Le et al adopted Faster-RCNN [13] architecture to detect cell-phone usage and driving with hands off the wheel. Koesdwiady et al in [14] used deeper VGG19 architecture to learn behaviour categories with softmax supervision.…”

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition

2018

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different spatial-temporal fusion strategies to combine the intra frame static clues and inter frame dynamic clues for final behaviour recognition. So as to validate the effectiveness of the designed spatial-temporal deep learning based model, we create a simulated driving behaviour dataset, containing 1237 videos with 6 different driving behavior for recognition. Experiment result shows that our proposed method obtains noticeable performance improvements compared to the existing methods.

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Multiple Scale Faster-RCNN Approach to Driver’s Cell-Phone Usage and Hands on Steering Wheel Detection

Cited by 97 publications

References 13 publications

Context-Aware Driver Distraction Severity Classification using LSTM Network

Context-Aware Driver Distraction Severity Classification using LSTM Network

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

Spatial-Temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition

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