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With the popularization of cars and motorcycles in recent years, the number of cars and motorcycles increases year by year. However, the increasing number of cars and motorcycles results in car accidents year after year. Hence, Advanced Driver Assistance Systems is widely used on cars. Wish to reduce the car accidents via the Driver Assistance Systems.
This dissertation focus on Driver Assistance Systems, which is based on analyze source images and detect vehicles in the rear. In academic research, it mostly uses infrared sensors to detect the tailing cars and decides whether to alarm according to the distance between the tailing cars and our own car doors. However, the defect of this method is that it may misjudge because while the sensor is alarming, the approaching car from the rear is moving away from the car door.
This dissertation proposes a rather more flexible system structure. It can sense the position of car door automatically and define a specific area to sense the approaching cars in it. Wish that once a cellphone or a camera is erected, the system can sense dangerous approaching cars from the back of car doors automatically.
The system in this dissertation does not depend on sensor sensing method but analyze the Dense Optical Flow of cameras or cellphone videos as features of incidents at the rear of car doors. We group the tracks of moving objects and find out the eigenvector of each group with the tracks and utilizing Adaboost and Support Vector Machine to determine whether dangerous events will happen.
In experiment, we display that the system, which is proposed in this dissertation has great reliability on detecting dangerous things in the back of car doors. Furthermore, in the video, dangerous events can be detected precisely, achieving the goal that a cellphone or a camera detects the dangerous approaching cars from the back of car doors. From practical operation, this system achieves 29 frames per second on personal computer. | en_US |