Visual quick identifying method for football robot

Abstract

The quick visual identifying method for football robot includes installing video camera over the football court to obtain shooting range covering the whole court; installing the video acquisition card in the workstation, connecting the input of the video acquisition card to the output of the video camera to output digital image data, and processing the digital image data in computer, so as to determine the goal and sideline position data, provide the real-time positions, directions, speeds and accelerations of the players in the court and the position data of the ball. The VC++MFC program includes the steps of: target characteristic extraction and correction parameter setting; image pre-treatment; target identification; and correction treatment. The identifying method of the present invention has the features of high search speed, high identifying precision, high environment adaptability, etc.

Description

technical field [0001] The invention is aimed at digital image processing and recognition when a robot plays a football game. Involves real-time software programming, digital signal processing, computer science and engineering optics and other fields. Background technique [0002] Robot soccer was formally proposed in 1992 by Professor Alan Mackworth of the University of British Columbia in Canada. This technology and entertainment project emerged in the 1990s and became popular in developed regions such as Europe, America and Japan. [0003] The most important purpose of the Robot Soccer Game is to test the cutting-edge research of artificial intelligence, especially the latest results of multi-agent system research, which can be transformed and applied to other industrial or civilian robots. It can be said that the football robot can be used as a standard platform for researching all robots and artificial intelligence. In addition, football itself is a very entertaining...

AI Technical Summary

Problems solved by technology

[0006] 1. At present, the color model used by most image recognition technologies is the red, green and blue RGB color space. With different light intensities, the same color will also change after being captured by the camera and image card. The RGB model is easily affected by the light source. The system cannot adapt to changes caused by the environment

[0007] 2. The wide-angle lens of the camera will produce barrel distortion, and the height difference between the robot and the ball will cause the position deviation of the robot, making the identified robot position inaccurate and affecting the accuracy of image recognition

[0008] 3. It will take a lot of time to recognize all the areas captured by the camera (that is, global recognition)

However, if the previous frame is not recognized or misrecognized, the target will search for the target with a wrong path for a long time, which will affect the recognition speed

Images