Seatbelt Unfasten Driving Detection Based On Image Processing And Machine Learning

Patten recognition applied in intelligent traffic system significantly help peo-ple soft traffic problem caused by increasingly large transport network and the large number of new camera.Recently, license plate recognition, through a red light, speeding, etc. have been identified to automate processing.The more in-depth identification such as a using cell phone while driving, not wearing a seat-belt driving detection, vehicle identification has just begun. This paper describes the entire process of handling not wearing a seatbelt driving detection. Paper is divided into three parts:1. The blue license plate detection based on the color and texture informa-tion and locating windows based on machine learning method.To detect belts, we must first locate the windows, and the windows and license plates have a relatively fixed position relationship.plate location is rela-tively simple, so we took a first positioning plate reorientation window approach. The color plates projected onto HSL color space. The area where color plates may sitting were detailed characterization, which filter out most of the picture area, and then take the Sobel operator horizontal maxima counting method to extract the region texture features. For the window locating process, using statistical learning methods to obtain the window detector.2.Seatbelt edge detection based on Canny edge detectionFor most of the image samples, Seatbelt edge is very clear, The problem is how fast we can screening of samples of these clear image.we filtered Canny edge detection result using gradient direction information, the angle between the line segment according to the detected, distance and other information to determine whether the seat belt edge, further, we have improved Hough circle detection, turn it into a pair of semi-circular arc on the detection,specify line location information, to achieve the effect of rapid screening.3.Seatbelt edge detection based on adaboost learning algorithm Non-statistical algorithm for texture, occluding and other disturbances are extremely sensitive, and approximately one-third or more of the picture more or less interference, and some from reflections of the front windows, some from clothes texture, some from the arm occluding, Therefore, we introduced a ma-chine learning algorithm, by constructing two distinct set of samples, by the same algorithm to obtain two classifiers, with different the test results, the two can be used together to achieve better results.