Drive-Net: Convolutional Network for Driver Distraction Detection

Majdi, Mohammad S.; Ram, Sundaresh; Gill, Jonathan T.; Rodríguez, Jeffrey J.

doi:10.1109/ssiai.2018.8470309

Cited by 51 publications

(35 citation statements)

References 9 publications

(11 reference statements)

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“…1). Majdi et al [26] proposed Drive-net, a method that uses a combination of a CNN and a random decision forest to classify driver images. Driver-net achieved a detection accuracy of 95% on the Kaggle dataset.…”

Section: Inceptionv3 Without a Classification Layermentioning

confidence: 99%

HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers

et al. 2020

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Distracted driving causes a large number of traffic accident fatalities and is becoming an increasingly important issue in recent research on traffic safety. Gesture patterns are less distinguishable in vehicles due to in-vehicle physical constraints and body occlusions from the drivers. However, by capitalizing on modern camera technology, convolutional neural network (CNN) can be used for visual analysis. In this paper, we present a hybrid CNN framework (HCF) to detect the behaviors of distracted drivers by using deep learning to process image features. To improve the accuracy of the driving activity detection system, we first apply a cooperative pretrained model that combines ResNet50, Inception V3 and Xception to extract driver behavior features based on transfer learning. Second, because the features extracted by pretrained models are independent, we concatenate the extracted features to obtain comprehensive information. Finally, we train the fully connected layers of the HCF to filter out anomalies and hand movements associated with nondistracted driving. We apply an improved dropout algorithm to prevent the proposed HCF from overfitting to the training data. During the evaluation, we apply the class activation mapping (CAM) technique to highlight the feature area involving ten tested classes of typical distracted driving behaviors. The experimental results show that the proposed HCF achieves the classification accuracy of 96.74% when detecting distracted driving behaviors, demonstrating that it can potentially help drivers maintain safe driving habits.

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Section: Inceptionv3 Without a Classification Layermentioning

confidence: 99%

HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers

et al. 2020

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“…The results showed that the system could achieve 96.31% accuracy on the AUC Distracted Driver dataset. However, in recent works [19,20,[24][25][26], only spatial information was considered, but temporal information, related to important cues for behavior recognition, were discarded. In Reference [48], Chuang et al proposed a skeleton-based and a point-cloud approach with multiple views based on Kinect depth cameras for driver behavior recognition, and LSTM was adopted to train the behavior model.…”

Section: Related Workmentioning

confidence: 99%

“…Although distracted behavior analysis was studied in recent works [19,[24][25][26], only spatial information was considered while temporal information was discarded. In previous studies of action recognition, many network configurations were designed to integrate the spatial and temporal information, for example, 2D ConvNets + LSTM [41,42], 3D ConvNet [43,44], and two-stream network [27].…”

Section: Temporal Stream Convnetmentioning

confidence: 99%

“…Then, in Reference [18], the authors proposed DarNet and investigated the mixing of different models, i.e., CNNs, recurrent neural network (RNNs), and SVMs, to detect driver distraction. In 2018, Majdi et al proposed Drive-Net to classify 10 distracted behaviors, which was composed of a convolutional neural network (CNN) and a random decision forest [19]. Tran et al [20] utilized four different CNN models including VGG-16 [21], AlexNet [12], GoogleNet [22,23], and a residual network to classify 10 distracted behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…The trade-off between accuracy and efficiency remains an issue. In addition, only spatial information was considered, but temporal information, related to important cues for behavior recognition, was ignored in recent works [19,20,[24][25][26]. Although behavior analysis or action recognition was studied for many years in the computer vision field, driver behavior analysis is a specific issue and is challenging due to the light changing, occlusion, clutter, and subtle actions.…”

Section: Introductionmentioning

confidence: 99%

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Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network

et al. 2020

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According to the World Health Organization global status report on road safety, traffic accidents are the eighth leading cause of death in the world, and nearly one-fifth of the traffic accidents were cause by driver distractions. Inspired by the famous two-stream convolutional neural network (CNN) model, we propose a driver behavior analysis system using one spatial stream ConvNet to extract the spatial features and one temporal stream ConvNet to capture the driver’s motion information. Instead of using three-dimensional (3D) ConvNet, which would suffer from large parameters and the lack of a pre-trained model, two-dimensional (2D) ConvNet is used to construct the spatial and temporal ConvNet streams, and they were pre-trained by the large-scale ImageNet. In addition, in order to integrate different modalities, the feature-level fusion methodology was applied, and a fusion network was designed to integrate the spatial and temporal features for further classification. Moreover, a self-compiled dataset of 10 actions in the vehicle was established. According to the experimental results, the proposed system can increase the accuracy rate by nearly 30% compared to the two-stream CNN model with a score-level fusion.

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Computer vision‐based recognition of driver distraction: A review

Moslemi

Soryani

Azmi

2021

Concurrency and Computation

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Vehicle crash rates caused by distracted driving have been rising in recent years. Hence, safety while driving on roads is today a crucial concern across the world. Some of the reasons due to which drivers may lose attention include the use of mobile phones, speaking with passengers, and reaching behind to grab something while driving. There are various types of distractions, out of which we focus on manual ones based on the posture of the driver. This work presents a review on open problems and challenges concerning the vision-based distraction recognition of drivers. Types of experimental environments are described; and image modalities, public and custom datasets and body parts inspected during distraction recognition are explored. Besides, a comparative review of different works on vision-based recognition of driver's manual distractions and limitations for each dataset is presented. Main approaches of vision-based manual distractive driving recognition can be categorized into conventional and deep learning methods. These approaches are compared and classified based on whether using temporal information or not. Finally, we give some suggestions for improvement, and look forward to future development directions of the vision-based recognition of driver's manual distractions. K E Y W O R D Sdistraction recognition, driver dataset, driver gesture, driver monitoring, driving simulator INTRODUCTIONDistractions decrease driver's awareness and decision-making; lead to increased workload, delayed responses to driving events, speed and lane maintenance disruptions. 1 Distracted drivers would lose their vehicle control, resulting in crashing into vehicles or stationary objects. 2 In other words, such a driver loses vigilance regarding the driving situation, leading to a higher collision risk 3 ; then the safety of the driver and other passengers are affected. The National Highway Traffic Safety Administration (NHTSA) 4 has reported three activities, including dialing, browsing, and texting, which make drivers have their eyes off the road for a longer time, lead to threefold crash risk. Performing high cognitive load tasks while driving impacts the driver's visual behavior and driving performance. Drivers under high cognitive loads spend less time checking mirrors, traffic lights instruments, and areas around intersections. 5 This results in unintended lane drifts, overlooking other traffic participants and, in the worst scenario, crashes. 6 Rapid improvements in computers and wireless technologies have made complex and various devices that people can use while driving.Notably, in the field of entertainment, information and driver assistance, studies have proposed numerous functions. However, using such functions during driving could distract the driver from focusing on driving itself. 6 The widespread usage of cell phones, navigation, and infotainment systems has escalated the problem of distraction. Therefore, the cognitive load caused by secondary tasks has increased. Multitasking is today an essential element of ind...

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Drive-Net: Convolutional Network for Driver Distraction Detection

Cited by 51 publications

References 9 publications

HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers

HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers

Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network

Computer vision‐based recognition of driver distraction: A review

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