| dc.description.abstract | The concern for traffic safety is as old as the automobile history, and many are the efforts of carmakers, public agencies and research in order to decrease the number of accidents and victims of traffic accidents. Many of the accidents that happen are attributed to human failure. Because of reckless driving and / or malpractice, they can not see obstacles with enough time to avoid a collision. There are many types of obstacles: a vehicle, a pedestrian, a tree, or even an animal. Any object that obstructs the passage of the driver can cause an accident. This work is focused on identifying only other vehicles as obstacles. This work presents an algorithm capable of detecting an obstacle on the track by computer vision. The project uses a vehicle equipped with a monocular camera, for processing and identification of obstacles in real time, supporting the driver about the presence of them on the road, and alarting him about collision risks. Other sensors, such as radar, infrared, or sonar could assist in obstacle detecting, however, the premise of this study is to develop an algorithm using low-cost resources and focused on image processing. Initially, we will start with the delineation of the region for obstacles search, also called the region of interest (ROI), by detecting the runway lanes. Next, the detector will work on hypothesis generation (HG), identifying candidates for obstacles, and then processing them on the hypothesis verification stage, to confirm or deny the presence of real obstacles. The main attributes considered are image color / intensity, symmetry, edges, borders, horizontal and vertical lines and camera calibration. Also, using Haar Like Features, the classifier cascade was trained. | en |
