Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos

Seshadri, Keshav; Juefei-Xu, Felix; Pal, Dipan K.; Savvides, Marios; Thor, Craig P.

doi:10.1109/cvprw.2015.7301397

Cited by 93 publications

(44 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Compared to other commonly used distance measurement such as ℓ 1 -norm, ℓ 2 -norm, NCD exhibits the best result [48][49][50][51][52][53][54][55]68,69]. The result of each algorithm is a similarity matrix whose entry SimM ij is the NCD between the feature vector of probe image i and target image j.…”

Section: Experimental Setup Overviewmentioning

confidence: 97%

Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition

Juefei-Xu

Savvides

2016

Pattern Recognition

Self Cite

View full text Add to dashboard Cite

Section: Experimental Setup Overviewmentioning

confidence: 97%

Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition

Juefei-Xu

Savvides

2016

Pattern Recognition

Self Cite

View full text Add to dashboard Cite

“…Their proposed solution runs at a 0.06, and 0.09 frames per second for cell-phone usage, and "hands on the wheel" detection. [26] tackles the problem of cell phone usage detection. Their approach doesn't hold any static assumptions though (i.e.…”

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

226

112

View full text Add to dashboard Cite

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.

show abstract

“…In connection with secondary tasks recognition, different computer vision algorithms have been proposed in order to detect cell phone usage of the driver while driving [96,97,98,99,100]. High recognition rates are usually obtained (from 86.19% to 95%) using very different approaches.…”

Section: Biomechanical Distractionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

View full text Add to dashboard Cite

Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

show abstract

Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos

Cited by 93 publications

References 22 publications

Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition

Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

Driver Distraction Using Visual-Based Sensors and Algorithms

Contact Info

Product

Resources

About