44 research outputs found
Drowsiness Monitoring System Using Artificial lntelligent Technique.
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Drowsiness monitoring system using artificial intelligent technique
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A drowsiness detection method based on displacement and gradient vectors
This paper presents a drowsiness detection method for drivers based on visual features in a video sequence. Image
intensities are traditionally visual features. However, it is known that they are directly influenced by lighting conditions. We
propose a human eye detection method using the normalized cross-correlation between displacement vectors and gradient
vectors. Gradient vectors are dependent on lighting conditions but the normalization step makes them independent of
illuminations. In this way, the proposed method can detect human eyes regardless of various lighting conditions. We have also
found that normalized cross-correlation can be useful, not only for detecting eyes, but also for recognizing open and closed eye
states. To overcome poor lighting conditions, we used infrared auxiliary illumination in order to make the proposed method work
every moment. The computation speed of the proposed method is fast enough to perform at video rates
On Assisted Living of Paralyzed Persons through Real-Time Eye Features Tracking and Classification using Support Vector Machines
Background: The eye features like eye-blink and eyeball movements can be used as a module in assisted living systems that allow a class of physically challenged people speaks â using their eyes. The objective of this work is to design a real-time customized keyboard to be used by a physically challenged person to speak to the outside world, for example, to enable a computer to read a story or a document, do gaming and exercise of nerves, etc., through eye features tracking
Method: In a paralyzed person environment, the right-left, up-down eyeball movements act like a scroll and eye blink as a nod. The eye features are tracked using Support Vector Machines (SVMs).
Results: A prototype keyboard is custom-designed to work with eye-blink detection and eyeball-movement tracking using Support Vector Machines (SVMs) and tested in a typical paralyzed person-environment under varied lighting conditions. Tests performed on male and female subjects of different ages showed results with a success rate of 92%.
Conclusions: Since the system needs about 2 seconds to process one command, real-time use is not required. The efficiency can be improved through the use of a depth sensor camera, faster processor environment, or motion estimation
Driver attention and behaviour monitoring with the Microsoft Kinect sensor
Modern vehicles are designed to protect occupants in the event of a crash with some vehicles better at this than others. However, passenger protection during an accident has shown to be not enough in many high impact crashes. Statistics have shown that the human error is the number one contributor to road accidents. This research study explores how driver error can be reduced through technology which observes driver behaviour and reacts when certain unwanted patterns in behaviour have been detected. Finally a system that detects driver fatigue and driver distraction has been developed using non-invasive machine vision concepts to monitor observable driver behaviour.Electrical EngineeringM. Tech. (Electrical Engineering
Effect of cooking time on physical properties of almond milk-based lemak cili api gravy
One of the crucial elements in developing or reformulating product is to maintain the quality throughout its entire shelf life. This study aims to determine the effect of different cooking time on the almond milk-based of lemak cili api gravy. Various cooking times of 5, 10, 15, 20, 25 and 30 minutes were employed to the almond milk-based lemak cili api gravy followed by determination of their effects on physical properties such as total soluble solids content, pH and colour. pH was determined by using a pH meter. Refractometer was used to evaluate the total soluble solids content of almond milk-based lemak cili api gravy. The colours were determined by using spectrophotometer which expressed as L*, a* and b* values. Results showed that almond milk-based lemak cili api gravy has constant values of total soluble solids with pH range of 5 to 6, which can be classified as low acid food. Colour analysis showed that the lightness (L*) and yellowness (b*) are significantly increased while redness (a*) decreased. In conclusion, this study shows that physical properties of almond milk-based lemak cili api gravy changes by increasing the cooking time
A framework for context-aware driver status assessment systems
The automotive industry is actively supporting research and innovation to meet manufacturers' requirements related to safety issues, performance and environment. The Green ITS project is among the efforts in that regard.
Safety is a major customer and manufacturer concern. Therefore, much effort have been directed to developing cutting-edge technologies able to assess driver status in term of alertness and suitability. In that regard, we aim to create with this thesis a framework for a context-aware driver status assessment system. Context-aware means that the machine uses background information about the driver and environmental conditions to better ascertain and understand driver status. The system also relies on multiple sensors, mainly video and audio. Using context and multi-sensor data, we need to perform multi-modal analysis and data fusion in order to infer as much knowledge as possible about the driver. Last, the project is to be continued by other students, so the system should be modular and well-documented.
With this in mind, a driving simulator integrating multiple sensors was built. This simulator is a starting point for experimentation related to driver status assessment, and a prototype of software for real-time driver status assessment is integrated to the platform.
To make the system context-aware, we designed a driver identification module based on audio-visual data fusion. Thus, at the beginning of driving sessions, the users are identified and background knowledge about them is loaded to better understand and analyze their behavior.
A driver status assessment system was then constructed based on two different modules. The first one is for driver fatigue detection, based on an infrared camera. Fatigue is inferred via percentage of eye closure, which is the best indicator of fatigue for vision systems. The second one is a driver distraction recognition system, based on a Kinect sensor. Using body, head, and facial expressions, a fusion strategy is employed to deduce the type of distraction a driver is subject to. Of course, fatigue and distraction are only a fraction of all possible drivers' states, but these two aspects have been studied here primarily because of their dramatic impact on traffic safety.
Through experimental results, we show that our system is efficient for driver identification and driver inattention detection tasks. Nevertheless, it is also very modular and could be further complemented by driver status analysis, context or additional sensor acquisition