Abstract
With the rapid development of the sensors and computer technology, pattern recognition becomes the core content of the modern intelligent transportation. And vehicle image recognition in the field of transport has been more and more attention and development.
This article describes the image preprocessing, image segmentation, the extraction of the background image from the image detection and moving objects, building of a neural network and classification tree model, and identification of people and vehicles. The technology of preprocess includes gray image, image binarization, image gradient sharpening and image de-noising processing technology. As the background is static, object detection takes the method of background subtraction. This paper compares two completely different algorithms of the neural network and classification tree. The classification tree is easier to understand and simpler. But neural network has very strong self-adaptive, self-learning function and better accuracy. Finally, the neural network model is built to process image recognition.
This project uses the VS2005 as its development instrument and the Opencv as its moving platform. The study builds a simple intelligent surveillance system, which is based on the motion detection under a fixed background subtraction and vehicle identification technology on a certain section of road.
