A Deep Learning Approach to Driver Distraction Detection of Using Mobile Phone

Xiong, Qunfang; Lin, Jun; Wei, Yue; Liu, Shiwang; Liu, Yue; Ding, Chi

doi:10.1109/vppc46532.2019.8952474

Cited by 19 publications

(19 citation statements)

References 7 publications

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“…The reviewed AmI literature is good evidence of that. Although the works specifically identified along those lines [64,68,69,[77][78][79]82,85,96,102,105,106] cover only a limited subset of the broad spectrum of applications resulting from the technological progress in the field, they provide good intuition on the relevance of object detection as a key factor for making both vehicles and infrastructures safer, more efficient, comfortable and reliable. In particular, in-vehicle ITS systems [64,[77][78][79]85,96,102,105,106] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest.…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…Although the works specifically identified along those lines [64,68,69,[77][78][79]82,85,96,102,105,106] cover only a limited subset of the broad spectrum of applications resulting from the technological progress in the field, they provide good intuition on the relevance of object detection as a key factor for making both vehicles and infrastructures safer, more efficient, comfortable and reliable. In particular, in-vehicle ITS systems [64,[77][78][79]85,96,102,105,106] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest. Such systems embed detection solutions conceived as safety mechanisms for monitoring both driver operations [77,78,85], preventing distractions, and ultimately, the loss of control of the vehicle, as well as on-road events [64,79,96,102,105,106], being the latter mainly implemented nowadays in the form of a warning instrument triggered in situations of potential collisions [79,96] or infractions [106], but also designed towards decision-making support in future autonomous vehicles [64,102,105].…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…In particular, in-vehicle ITS systems [64,[77][78][79]85,96,102,105,106] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest. Such systems embed detection solutions conceived as safety mechanisms for monitoring both driver operations [77,78,85], preventing distractions, and ultimately, the loss of control of the vehicle, as well as on-road events [64,79,96,102,105,106], being the latter mainly implemented nowadays in the form of a warning instrument triggered in situations of potential collisions [79,96] or infractions [106], but also designed towards decision-making support in future autonomous vehicles [64,102,105]. For its part, regarding conventional ITS alternatives [68,69,82], the different approaches identified are aimed at road traffic control and early response to emergencies, applications in which the use of UAVs has been intensively explored [68,69] due to their capabilities to record and communicate information in a non-intrusive way, as well as to recognize areas inaccessible by other means.…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…The result is a simplified and smaller architecture, conceived as a single feedforward neural network, suitable to derive high-inference-speed detection models. The unified approach, crystallized in compact architectures considered a standard nowadays, such as SSD [118] and YOLO in its different versions [119][120][121][122], constitute the foundation on which modern lightweight object detection architectures have been built in AmI [48,49,52,54,55,63,65,66,68,70,72,73,75,[77][78][79][80][81][82][86][87][88]90,91,94,100,[105][106][107]. Such compact architectures, however, resulted from an eminently moderate structural optimization in a first attempt to make CNNs more manageable, prioritizing accuracy preservation over structural reduction and inference speed increase.…”

Section: Architecturesmentioning

confidence: 99%

“…The study revealed two different approaches devised in response to the noted datarelated shortcomings: the creation of ad hoc datasets for the problem addressed [50,64,68,[77][78][79][80]84,86,91,95,98,100,103,109], and data augmentation [48,50,52,53,57,60,61,64,70,71,73,74,76,81,84,85,87,97,98,100,106]. As shown in Table 3, both solutions have been widely adopted by the authors, a fact quite surprising considering the substantial effort typically required to address the various tasks involved in creating new datasets (image and video acquisition and homogenization, but fundamentally, the creation of annotations).…”

Section: Improved Ad Hoc Datasetsmentioning

confidence: 99%

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On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

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Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used.

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Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

Section: Improved Ad Hoc Datasetsmentioning

confidence: 99%

See 3 more Smart Citations

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

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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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