Graphical model based facial feature point tracking in a vehicle environment

Facial feature point tracking is a research area that can be used in human-computer interaction (HCI), facial expression analysis, fatigue detection, etc. In this paper, a statistical method for facial feature point tracking is proposed. Feature point tracking is a challenging topic in case of uncertain data because of noise and/or occlusions. With this motivation, a graphical model that incorporates not only temporal information about feature point movements, but also information about the spatial relationships between such points is built. Based on this model, an algorithm that achieves feature point tracking through a video observation sequence is implemented. The proposed method is applied on 2D gray scale real video sequences taken in a vehicle environment and the superiority of this approach over existing techniques is demonstrated

Linear and non-linear dynamic analyses of sandwich panels with face sheet-tocore debonding

А survey of recent developments in the dynamic analysis of sandwich panels with face sheet-to-core debonding is presented. The finite element method within the ABAQUSTM code is utilized. The emphasis is directed to the procedures used to elaborate linear and non-linear models and to predict dynamic response of the sandwich panels. Recently developed models are presented, which can be applied for structural health monitoring algorithms of real-scale sandwich panels. First, various popular theories of intact sandwich panels are briefly mentioned and a model is proposed to effectively analyse the modal dynamics of debonded and damaged (due to impact) sandwich panels. The influence of debonding size, form and location, and number of such damage on the modal characteristics of sandwich panels are shown. For nonlinear analysis, models based on implicit and explicit time integration schemes are presented and dynamic response gained with those models are discussed. Finally, questions related to debonding progression at the face sheet-core interface when dynamic loading continues with time are briefly highlighted

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

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.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

A Low Cost Real Time Embedded Control System Design Using Infrared Signal Processing with Application to Vehicle Accident Prevention

Vehicle accidents are most common if the driving is inadequate. These happen on most factors if the driver is drowsy or if heis alcoholic. Driver drowsiness is recognized as an important factor in the vehicle accidents. It was demonstrated that drivingperformance deteriorates with increased drowsiness with resulting crashes constituting more than 20% of all vehicleaccidents. But the life lost once cannot be re-winded. Advanced technology offers some hope avoid these up to some extent.A car simulator study was designed to collect physiological data for validation of this technology. Methodology for analysisof physiological data, independent assessment of driver drowsiness and development of drowsiness detection algorithm bymeans of sequential fitting and selection of regression models is presented. In this paper proposes an approach towardsdesign of a Low cost real time embedded control system which involves measure and controls the eye blink using sensor. Ascar manufacturers / industrial automotive communities, incorporate intelligent vehicle systems in order to satisfy theconsumer’s ever increasing demand for more assistant systems for comfort, navigation, or communication, to address theissue of increased level of cognitive stress on drivers to the sources of distraction from the most basic task at hand, i.e.,driving the vehicle. Driver’s drowsiness detection systems are actually receiving a large interest in the academic andindustrial automotive communities for their potentiality to reduce fatalities Eye detection is a crucial aspect in many usefulapplications ranging from face recognition / detection to human computer interface for, driver behavior analysis. Visionbaseddriver fatigue detection which is non-contact has a key advantage over applicability. In this paper proposes a simpleand economical prototype design as a solution in developing a intelligent vehicles based on IR signal processing formonitoring the driver’s drowsiness level, vigilance and alerting the driver to prevent accidents. This approach is economicaland all the lower income side vehicle owners can afford to installation of this system.Keywords- Intelligent Vehicles, Driver Vigilance, Human fatigue, Safe Navigatio