A Multiprocessor Platform Based on FPGA Technology Targeted for a Driver Vigilance Monitoring Device

Medical devices processing images or audio or executing complex AI algorithms are able to run more efficiently and meet real time requirements if the parallelism in those algorithms is exploited. In this research a methodology is proposed to exploit the flexibility and short design cycle of FPGAs (Field Programmable Gate Arrays) in order to achieve this target. Hardware/software co-design and dynamic partitioning allow the optimization of the multiprocessor platform design parameters and software code targeting each core to meet real time constraints. This is practically demonstrated by building a real life driver vigilance monitoring system based on visual cues extraction and evaluation. The application drives the whole design process to prove its effectiveness. An algorithm was built to achieve the goal of detecting the eye state of the driver (open or closed) and it is applied on captured consecutive frames to evaluate the vigilance state of the driver. Vigilance state is measured depending on duration of eye closure. This video processing application is then targeted to run on a multi-core FPGA based processing platform using the proposed methodology. Results obtained were very good using the Grimace Face Database and when operating the system on one’s face. On operating the device, a false positive of eye closure must take place two consecutive times in order to get an alarm, which decreases the probability of failure. The timing analysis applied proved the importance of using the concept of parallelism to achieve performance constraints. FPGA technology proved to be a very powerful prototyping tool for complex multiprocessor systems design. The flexible FPGA technology coupled with hardware/software co-design provided means to explore the design space and reach decisions that satisfy the design constraints with minimum time investment and cost

A high speed Tri-Vision system for automotive applications

Purpose: Cameras are excellent ways of non-invasively monitoring the interior and exterior of vehicles. In particular, high speed stereovision and multivision systems are important for transport applications such as driver eye tracking or collision avoidance. This paper addresses the synchronisation problem which arises when multivision camera systems are used to capture the high speed motion common in such applications. Methods: An experimental, high-speed tri-vision camera system intended for real-time driver eye-blink and saccade measurement was designed, developed, implemented and tested using prototype, ultra-high dynamic range, automotive-grade image sensors specifically developed by E2V (formerly Atmel) Grenoble SA as part of the European FP6 project – sensation (advanced sensor development for attention stress, vigilance and sleep/wakefulness monitoring). Results : The developed system can sustain frame rates of 59.8 Hz at the full stereovision resolution of 1280 × 480 but this can reach 750 Hz when a 10 k pixel Region of Interest (ROI) is used, with a maximum global shutter speed of 1/48000 s and a shutter efficiency of 99.7%. The data can be reliably transmitted uncompressed over standard copper Camera-Link® cables over 5 metres. The synchronisation error between the left and right stereo images is less than 100 ps and this has been verified both electrically and optically. Synchronisation is automatically established at boot-up and maintained during resolution changes. A third camera in the set can be configured independently. The dynamic range of the 10bit sensors exceeds 123 dB with a spectral sensitivity extending well into the infra-red range. Conclusion: The system was subjected to a comprehensive testing protocol, which confirms that the salient requirements for the driver monitoring application are adequately met and in some respects, exceeded. The synchronisation technique presented may also benefit several other automotive stereovision applications including near and far-field obstacle detection and collision avoidance, road condition monitoring and others.Partially funded by the EU FP6 through the IST-507231 SENSATION project.peer-reviewe

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

A Survey on Drivers Drowsiness Detection Techniques

Nowadays, there are many systems are available in market like navigation systems, warning alarm systems etc. to make drivers work easy. Traffic accidents due to human errors cause many deaths and injuries around the world. Drowsiness and sleeping while driving are now identified as one of the reasons behind fatal crashes and highway accidents caused by drivers. Various drowsiness detection techniques research are discussed in this paper. These techniques are classified and then compared using their features. Computer vision bas ed image processing techniques is one of them. This uses various images of driver to detect drowsiness states using his/her eyes states and facial expressions. This technique is on the focus of this survey paper

Towards hybrid driver state monitoring : review, future perspectives and the role of consumer electronics

The purpose of this paper is to bring together multiple literature sources which present innovative methodologies for the assessment of driver state, driving context and performance by means of technology within a vehicle and consumer electronic devices. It also provides an overview of ongoing research and trends in the area of driver state monitoring. As part of this review a model of a hybrid driver state monitoring system is proposed. The model incorporates technology within a vehicle and multiple broughtin devices for enhanced validity and reliability of recorded data. Additionally, the model draws upon requirement of data fusion in order to generate unified driver state indicator(-s) that could be used to modify in-vehicle information and safety systems hence, make them driver state adaptable. Such modification could help to reach optimal driving performance in a particular driving situation. To conclude, we discuss the advantages of integrating hybrid driver state monitoring system into a vehicle and suggest future areas of research

How Facial Features Convey Attention in Stationary Environments

Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open-source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we created several prediction models for user attention and identified the Histogram of Oriented Gradients (HOG) and Action Units to be the greatest predictors of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNNs and CRNNs). Interestingly, CRNNs did not appear to perform significantly better than their CNN counterparts. While deep learning methods achieved greater prediction accuracy, SVMs utilized less resources and, using certain parameters, were able to approach the performance of deep learning methods