Driver errors and carelessness contribute most of the road accidents occurring nowadays. The major driver errors are caused by drowsiness, drunken and reckless behavior of the driver. This paper focuses on a driver drowsiness detection system in Intelligent Transportation System, which focuses on abnormal behavior exhibited by the driver using Raspberry pi single board computer. The capability of driving support systems to detect the level of driver’s alertness is very important in ensuring road safety. By observation of blink pattern and eye movements, driver fatigue can be detected early enough to prevent collisions caused by drowsiness. In the proposed system a non-intrusive driver drowsiness monitoring system has been developed using computer vision techniques. Based on the simulation results, it was found that the system has been able to detect drowsiness in spite of driver wearing spectacles as well as the darkness level inside the vehicle. Moreover the system is capable of detecting drowsiness within time duration of about two seconds. The detected abnormal behavior is corrected through alarms in real time. We have also done a transformation of an image from 2d to 3d using wavelet analysis. Here we also compared the wavelet technique with other techniques namely stereo-photogrammetric & edge information technique. The image conversion from 2d to 3d can be done by finding the edges of an image. This edge detection can be used in various fields of image processing, image analysis, image pattern recognition, and computer vision, as well as in human vision. In this, we did our experiment using wavelet technique and the results when compared with the stereo photogrammetry and edge information techniques we find that the wavelet technique gives better result. From the past work, we have observed that Wavelet analysis is an easy method for 2D to 3D analysis.