Video-based Traffic Parameter Detection Research

Traffic data collection based on video image has been receiving attention from researchers in the fields of computer vision during the past few years, and it has been new research directions in the fields of computer vision. Traffic data collection is a very important part of the intelligent transport system and has a vast perspective of application. At the same time, traffic data collection based on video image involves a lot of research areas such as image processing, computer vision, pattern recognition etc. It has high value in the theory of computer vision and has been a hotspot in the circle of international science.This paper is aimed at extracting some important traffic parameters using traffic video sequences, which are acquired through a static monocular camera in specific traffic scenes. On this thesis some key techniques including moving object detection from image series, target casting shadow detection as well as traffic data collection are focused on. Finally a traffic parameter measurement system is designed and realized.Vehicle target detection is one of the most important parts of traffic parameter measurement. Moving target detection is popular method in solving this question. In this paper, the technique features of the moving target algorithms including adjacent frame difference algorithm, background difference algorithm and model-based algorithm are analyzed and experiments are made to verify this features. Based on this analysis, an improved model based algorithm is proposed which effectively solve the problem of model update after changing of the background.The shadow of the moving object has similar features in vision and motion as the moving object itself. The algorithm we analyzed cannot detect the difference between the moving object and its shadow, so we need perform shadow detection. The shadow model and main shadow detection algorithm is introduced in this paper. And the shadow detection algorithm based on HSV spaces is discussed in detail; experiment is given to verify the validity of this algorithm.