Monocular-vision-based study on moving object detection and tracking

In this paper, we present an approach for recognizing and tracking a moving color object which break into the robot´s camera vision in the mobile robot. In order to obtain input feature for moving-object recognition, we perform image processing of the video sequence by background substraction method. Given the images of video sequence provided by a fixed camera on mobile robot, disparity images in different time are integrated into a tracking framework in order to find the HSV space based color features of the moving object. Based on the color object´s moving and background substraction method, an color classifier based on the HS thresholds is trained to detect moving object. We show experimentally that the moving-object recognition performance can be improved significantly by using information about object´s edge as an additional feature. Through the recognition, we can find the center of the moving object and control the mobile robot´s angle and rate to realize the vision-tracking of moving object. Our system aims at applications in the field of robot intelligence,where it is important to do run-on recognition in real-time, to allow for robot sel-learning and not to rely on appointed features.