| dc.description.abstract | Traffic accidents occur frequently in modernized society so that the society has to pay a lot of cost. There are more than 60,000 vehicle accidents occurring each year in Taiwan. Among them, over ninety percent of accidents are caused due to the careless of drivers according to the statistical analysis.In this thesis, a surveillance-based system utilizing face detection, tracking, and hazardous status monitoring of drivers is designed. By monitoring the symptoms of non-concentration or fatigue of drivers, a warning signal can be issued in advance so as to preventing the occurring of accidents due to the lack of unawareness.
The proposed system is composed of four main parts. The first part is face detection. The modified version of YCbCr color space is adopted to obtain raw skin color images. Edge smoothing operation is employed to remedy the erroneous judgments of extracted features. Moreover, logarithmic intensity difference method is devised to compensate partial illumination. These algorithms make the task of face detection more robust and have higher accuracy than known skin-color model.
The second part is face tracking. Correlation operation is manipulated on current face and records the ones which can regulate the borders and increase the accuracy. Time complexity of the proposed method can be drastically decreased due to the performing of face tracking.
The third part is feature inspection and marking. Our proposed method can not only promote the accuracy but also mark the exact positions of features. A novel triangular-based theorem is adopted to calculate the angles of features to determine whether the considered face is frontal or profile. Moreover, it can conquer the problems of different face sizes, varying lighting conditions, varying expressions, and noises.
The forth part is the analysis of dangerous behaviors. According to statistical results conducted by the Ministry of Communications, different weighs are assigned for five hazardous events which may result in accidents. The proposed system can automatically analyze the hazardous behaviors of chatting, drowsing, phone using, consecutive head lowering, and facial occlusion by performing direction estimation, pupil detection, feature variation, etc.
Experiments were conducted on a variety of testing video sequences. An approximately 91% success rate can be achieved; besides with both false rejection rate and false acceptance rate being very low (near 10%). Experimental results reveal the feasibility and validity of our proposed system in monitoring various hazardous behaviors resulting from drivers. | en_US |