Journal ArticleDOI
Hybrid Embedded-Systems-Based Approach to in-Driver Drunk Status Detection Using Image Processing and Sensor Networks
TLDR
A system whose main objective is identifying a person having alcohol in the blood through supervised classification of sensor-generated and computer-vision-based data and reaches a classification performance of 98% while ensures adequate operation conditions for the embedded system is introduced.Abstract:
Car drivers under the influence of alcohol is one of the most common causes of road traffic accidents. To tackle this issue, an emerging, suitable alternative is the use of intelligent systems -traditionally based on either sensor networks or artificial vision- that are aimed to prevent starting the car when drunk status on the car driver is detected. In such vein, this paper introduces a system whose main objective is identifying a person having alcohol in the blood through supervised classification of sensor-generated and computer-vision-based data. To do so, some drunk-status criteria are considered, namely: the concentration of alcohol in the car environment, the facial temperature of the driver and the pupil width. Specifically, for data acquisition purposes, the proposed system incorporates a gas sensor, temperature sensor and a digital camera. Acquired data are analyzed into a two-stages machine learning system consisting of feature selection and supervised classification algorithms. Both acquisition and analysis stages are to be performed into a embedded system, and therefore all procedures and algorithms are designed to work at low-computational resources. As a remarkable outcome, due mainly to the incorporation of feature selection and relevance analysis stages, proposed approach reaches a classification performance of 98% while ensures adequate operation conditions for the embedded system.read more
Citations
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Journal ArticleDOI
Survey and synthesis of state of the art in driver monitoring.
TL;DR: In this article, the authors present a polychotomous view of DM through a pair of interlocked tables that relate these states to their indicators and the sensors that can access each of these indicators (e.g., a camera).
Book ChapterDOI
Driver Behavior Analysis: Abnormal Driving Detection Using MLP Classifier Applied to Outdoor Camera Images
Javier Alcalde,Javier Alcalde +1 more
TL;DR: In this paper , the authors presented a method to detect abnormal driving through standard deviations of the vehicle's trajectory, velocity, and acceleration, using the neural network Multi-Layer Perceptron (MLP) classifier.
Journal ArticleDOI
Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector
TL;DR: In this paper , the authors presented an effective data-driven anomaly detection scheme for drunk driving detection, which amalgamates the desirable features of the t-distributed stochastic neighbor embedding (t-SNE) as a feature extractor with the Isolation Forest (iF) scheme to detect drivers' drunkenness status.
Journal ArticleDOI
Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector
TL;DR: In this article , the authors presented an effective data-driven anomaly detection scheme for drunk driving detection, which amalgamates the desirable features of the t-distributed stochastic neighbor embedding (t-SNE) as a feature extractor with the Isolation Forest (iF) scheme to detect drivers' drunkenness status.
Proceedings ArticleDOI
Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving
Kevin Koch,Martin Maritsch,E. van Weenen,Stefan Feuerriegel,Matthias Pfäffli,Elgar Fleisch,Wolfgang Weinmann,Felix Wortmann +7 more
TL;DR: In this paper , the authors developed an in-vehicle machine learning system to predict critical BAC levels, which leverages driver monitoring cameras mandated in numerous countries worldwide, and evaluated their system with n = 30 participants in an interventional simulator study.
References
More filters
Journal ArticleDOI
Feature Selection for Classification
Manoranjan Dash,Huan Liu +1 more
TL;DR: This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications.
Journal ArticleDOI
Adaptive histogram equalization and its variations
Stephen M. Pizer,Stephen M. Pizer,E. Philip Amburn,E. Philip Amburn,John D. Austin,Robert Cromartie,Ari Geselowitz,Ari Geselowitz,Trey Greer,Bart M. ter Haar Romeny,John B. Zimmerman,John B. Zimmerman +11 more
TL;DR: It is concluded that clipped ahe should become a method of choice in medical imaging and probably also in other areas of digital imaging, and that clip ahe can be made adequately fast to be routinely applied in the normal display sequence.
Proceedings ArticleDOI
Joint Haar-like features for face detection
TL;DR: Experimental results show that the proposed joint Haar-like feature for detecting faces in images yields higher classification performance than Viola and Jones' detector; which uses a single feature for each weak classifier.
Journal ArticleDOI
Multiclass and Binary SVM Classification: Implications for Training and Classification Users
Ajay Mathur,Giles M. Foody +1 more
TL;DR: An approach for one-shot multi- class classification of multispectral data was evaluated and was more accurate than the approaches based on a series of binary classifications and had other advantages relative to the binary SVM-based approaches.
Book ChapterDOI
Supervised Classification Algorithms in Machine Learning: A Survey and Review
TL;DR: This paper tries to compare different types of classification algorithms precisely widely used ones on the basis of some basic conceptions though it is obvious that a complete and comprehensive review and survey of all the supervised learning classification algorithms possibly cannot be accomplished by a single paper.