No abstract

Computer vision is a wide area of theoretical research and technical methods connected with object detection, object tracking and object classification. In this article computer vision is considered in context of embedding it into automobiles in order to automate the road traffic process through video stream analysis. During road traffic it is vital to detect objects quickly and correctly, so the authors pay attention to the pattern recognition quality, especially to the visual information semantic integrity preservation. Their main purpose is to find the ways of its possible improvement respectively to three basic stages of the pattern recognition process. To avoid semantic integrity violations of information in the initial stage of the image analysis the authors propose normalization; in the second stage new clustering method was developed based on particle swarm optimization and k-means algorithm; in the final stage of the pattern recognition process the Haar cascade classifier was used with normalized training samples. The obtained image processing algorithm was implemented in case of blurred and noisy images and proved its effectiveness respectively to the visual information semantic integrity preservation.