31,953 research outputs found
Characterizing driving behavior using automatic visual analysis
In this work, we present the problem of rash driving detection algorithm
using a single wide angle camera sensor, particularly useful in the Indian
context. To our knowledge this rash driving problem has not been addressed
using Image processing techniques (existing works use other sensors such as
accelerometer). Car Image processing literature, though rich and mature, does
not address the rash driving problem. In this work-in-progress paper, we
present the need to address this problem, our approach and our future plans to
build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201
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
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
201
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