Traffic monitoring with computer vision

Nowadays roads and streets are getting overcrowded, especially in bigger cities. Hence the main goal of our project is to build a traffic monitoring system which is able to detect the movement of cars and to track and count the different vehicles by analyzing a camera picture with the help of computer vision. The real-time process (15-30 fps) of the video stream works at daylight. The traffic monitoring includes the following: it accepts the video from a file or a camera, marks the moving vehicles with rectangles, and counts them. The system consists of three subsystems, these are the: video subsystem, motion detector subsystem, display/control subsystem. To maximize the speed of the program, each subsystem runs on different threads. To accept the visual information DirectShow is used, and the image processing is partially done with the DirectCV wrapper [6] for OpenCV. For motion detection, the system uses a Gaussian mixture model with background segmentation. There is an opportunity to mask the monitoring area also. With the mask it is possible to filter out for example the sidewalks and the opposite traffic direction so that our system avoids these parts of the image. The mask area can be easily built by placing a sequence of points which are the vertexes of a polygon. To create a polygon from the sequence of points a hybrid algorithm is applied which combines the 2opt heuristic method and a genetic algorithm. Connected component labeling algorithm is used for object detection, which utilizes a decision tree and Union-Find data structure in order to achieve the best performance. Recently we are testing a shadow removal algorithm based on edge detection in order to detect the objects in traffic scenes more accurately.