56,725 research outputs found
An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver
In recent years, road accidents have increased significantly. One of the
major reasons for these accidents, as reported is driver fatigue. Due to
continuous and longtime driving, the driver gets exhausted and drowsy which may
lead to an accident. Therefore, there is a need for a system to measure the
fatigue level of driver and alert him when he/she feels drowsy to avoid
accidents. Thus, we propose a system which comprises of a camera installed on
the car dashboard. The camera detect the driver's face and observe the
alteration in its facial features and uses these features to observe the
fatigue level. Facial features include eyes and mouth. Principle Component
Analysis is thus implemented to reduce the features while minimizing the amount
of information lost. The parameters thus obtained are processed through Support
Vector Classifier for classifying the fatigue level. After that classifier
output is sent to the alert unit.Comment: 4 pages, 2 figures, edited version of published paper in IEEE ICITE
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Video surveillance for monitoring driver's fatigue and distraction
Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
Improving construction materials management practices in construction sites
Construction Materials Management is a vital function for improving productivity in construction projects. Poor materials management can often affect the overall construction time, quality and budget. Currently, the construction material management practice in Somalia is believed to be poorly performed. Lack of standardized construction materials management system is one of the key issues facing by the building industry in Mogadishu-Somalia. The aim of this study was to investigate the current practices of material management at construction sites in Mogadishu-Somalia. A questionnaire survey study design was used to explore construction materials management practices. Fifty questionnaires were distributed to project managers, project engineers, site engineers, engineer, and foreman, and they were received and analysed. The following data analysis techniques were used: descriptive statistics were conducted to report sample characteristics, reliability and validity analyses were performed to confirm robustness of the instrument, graphical presentation such as bar charts were developed, and finally Average Mean Index Scale were constructed. The study results reveals that, 46.7% of respondent’s organization obtain materials for sites without site requisition by site engineer provisions, while 28.9% of respondent’s organization procure materials for sites with site requisition by project manager provisions and 13.3% of respondent’s organization procure materials for site by engineer. The results indicated that currently there is no standardized and computerized construction materials management system applied in Somalia. The researcher concluded that all contracting companies are interested in using some techniques of managing construction materials such as creating and updating database for materials categories from local and international suppliers. Finally, researcher recommends to use computerized construction materials management systems to reduce effort and time, and to achieve more accurate results
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