Vision-Based Intelligent Scenery Object Recognition Applied to Autonomous Land Vehicle

• Main Authors: Ching-Te Lai, 賴錦德
• Other Authors: 駱榮欽
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/aetefz

碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 98 === In the paper, we propose a vision-based scenery object recognition system while the autonomous land vehicle (ALV) is navigating on a road, in order to support its path planning and locating. In the system, we use improved optical flow algorithm to extract the blocks of moving objects in sequential images, and we set them as the moving candidate blocks. By way of optical flow computing, we can find the moving behaviors and velocities of the blocks. At the same time, by the disparity map (DM), we obtain the height and width information of the blocks from a stereo pair of images. Then, according to the information of location and moving path of ALV, we can identify an area as a region of interest (ROI) in the image for the safety of navigation. In the study, we use multiple classifiers of neural network and template matching method. By way of matching with prior databases and multiple classifiers, we could recognize the attributes of all the detected blocks. Finally, if we verify that an obstacle does affect the safety of navigation, we set strategies to alarm the system to dodge it or to ignore it. The system architecture includes two horizontally parallel CCD video cameras in front of ALV and one desktop PC with capture cards in ALV. We obtain both sides of sequential images in the PC through the capture cards. After image preprocessing and computing by optical flow and DM, we analyze several types of image data. By the principle of an object oriented design (OOD), we set them as objects, and also create classes to describe the attributes, behaviors, and methods of them. Transforming it into actuality is like how ALV controls itself to the detected obstacles or nun-obstacles. We choose an improved optical flow algorithm to fit on the changing background of images, and we can also obtain moving behaviors and velocities of objects. By DM, we have height and width information of obstacles to set ALV’s navigational strategies. After computing by multiple classifiers and template matching, the system could recognize the attributes and the behaviors of the objects from the scenery images in ALV navigational environments.