In this paper, a video stabilization technique is presented. There are four steps in the proposed approach. We begin with extracting feature points from the input image using the Lowe SIFT (Scale Invariant Feature Transform) point detection technique. This set of feature points is then matched against the set of feature points detected in the previous image using the Wyk et al. RKHS (Reproducing Kernel Hilbert Space) graph matching technique. We can calculate the camera motion between the two images with the aid of a 3D motion model. Expected and unexpected components are separated using a motion taxonomy method. Finally, a full-frame technique to fill up blank image areas is applied to the transformed image.
In this paper, a vision system for monitoring driver's vigilance is presented. The level of vigilance is determined by integrating a number of facial parameters. In order to estimate these parameters, the facial features of eyes, mouth and head are first located in the input video sequence. The located facial features are then tracked over the subsequent images. Facial parameters are estimated during facial feature tracking. The estimated parametric values are collected and analyzed every fixed time interval to provide a real-time vigilance level of the driver. A series of experiments on real sequences were demonstrated to reveal the feasibility of the proposed system.
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