Trends Towards Automotive Electronic Vision Systems for Mitigation of Accidents in Safety Critical Situations

Hughes, Ciáran; O’Malley, Ronan; O’Cualain, Diarmaid; Glavin, Martin; Jones, Edward

doi:10.5772/12907

Cited by 5 publications

(4 citation statements)

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“…As a consequence, the number of traffic accidents has also increased. Statistics show that there is about 1.2 million fatalities and 50 million injuries related to road traffic accidents per year worldwide [1]. Statistics published by the Croatian Bureau of Statistics [2] reveal that in 2011 there were 418 killed and 18, 065 injured persons who were participating in traffic in the Republic of Croatia.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Road Traffic Parameters based on Multi-Vehicle Tracking

Kovačić¹,

Ivanjko²,

Jelušić³

2015

Proceedings of the Croatian Computer Vision Workshop

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Development of computing power and cheap video cameras enabled today's traffic management systems to include more cameras and computer vision based applications for monitoring and control of road transportation systems. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow volume, origin-destination matrices, classify vehicles, etc. In this paper we propose a system for measurement of road traffic parameters (basic motion model parameters and macro-scopic traffic parameters). The system is based on Local Binary Pattern image features classification with a cascade of Gentle Adaboost classifiers to determine vehicle existence and its location in an image. Additionally, vehicle tracking and counting in a road traffic video is performed by using Extended Kalman Filter and virtual markers. The newly proposed system is compared with a system based on background subtraction. Comparison is performed by means of evaluating execution time and accuracy.

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Section: Introductionmentioning

confidence: 99%

Measurement of Road Traffic Parameters based on Multi-Vehicle Tracking

Kovačić¹,

Ivanjko²,

Jelušić³

2015

Proceedings of the Croatian Computer Vision Workshop

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“…Statistics show that there is about 1.2 million fatalities and 50 million injuries related to road traffic accidents per year worldwide [1]. Also there were 3 961 pedestrian fatalities in 2005 in the EU.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, there has been approximately 500 casualties a year in EU because of driver's lack of visibility in the blind zone. Data published in [1] estimated that there has been around 244 000 police reported crashes and 224 fatalities related to lane switching in 1991. Statistics published by the Croatian Bureau of Statistics [2] reveal that in 2011 there were 418 killed and 18 065 injured persons who were participating in the traffic of Croatia.…”

Section: Introductionmentioning

confidence: 99%

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Computer Vision Systems in Road Vehicles: A Review

Kovačić¹,

Ivanjko²,

Gold³

2014

Proceedings of the Croatian Computer Vision Workshop

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The number of road vehicles significantly increased in recent decades. This trend accompanied a build-up of road infrastructure and development of various control systems to increase road traffic safety, road capacity and travel comfort. In traffic safety significant development has been made and today's systems more and more include cameras and computer vision methods. Cameras are used as part of the road infrastructure or in vehicles. In this paper a review on computer vision systems in vehicles from the stand point of traffic engineering is given. Safety problems of road vehicles are presented, current state of the art in-vehicle vision systems is described and open problems with future research directions are discussed.

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PDE Based Dense Depth Estimation for Stereo Fisheye Image Pair and Uncertainty Quantification

Hiremath

2020

Computational Science and Its Applications – ICCSA 2020

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Dense estimation of depth from stereo pinhole image pair is a well known problem and seems to be easily solvable by both classical as well as machine learning algorithms. However, dense depth estimation from stereo fisheye image pair is apparently a more challenging problem. There are two main factors adding to the complexity of the problem: (I) owing to the wider field of view, the fisheye lenses are inherently nonlinear thus making the estimation problem harder. (II) because we want to estimate depth for every pixel coordinate from just two images, i.e. a calibrated stereo pair, the data is statistically insufficient thus greatly complexifying the problem. To alleviate (II) many depth estimation algorithms enlarge their parameter space by incorporating pose parameters and work on image sequence (esp. videos). Here we stick to the stereo camera setting and provide a novel estimation technique and quantify its uncertainty. We use the technique of variational calculus to derive a (noisy) pde for which wellposedness is proved and then solved in the least square sense using proximal algorithms. This setting enables us to invoke the standard machinery of nonlinear least squares for generating the covariance estimates. Most state of the art algorithms that are based on deep learning (nonlinear regression) technique fail to say anything about the uncertainty of the outputs which is of paramount importance in safety critical applications for e.g. arising in automotive domain. Lastly, our methodology has the advantage of being massively parallelizable for hardware implementation thereby offering lesser runtime.

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Trends Towards Automotive Electronic Vision Systems for Mitigation of Accidents in Safety Critical Situations

Cited by 5 publications

References 50 publications

Measurement of Road Traffic Parameters based on Multi-Vehicle Tracking

Measurement of Road Traffic Parameters based on Multi-Vehicle Tracking

Computer Vision Systems in Road Vehicles: A Review

PDE Based Dense Depth Estimation for Stereo Fisheye Image Pair and Uncertainty Quantification

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