Study Of Moving Object Detecting And Tracking Algorithms Based On Robot Vision

Visual information has accounted for an extremely great proportion in the human environment where dynamic visual information is its main constituent. The robot vision is a new direction in robotics area. Dynamic visual information based on the robot vision has become an important research direction. Detecting and tracking the moving object is a very important topic in the application field of vision, which has the widespread application in robotics domains. Because of exiting the phenomenons such as the illumination change, the background disturbance, the shadow, the camera vibration, the movement object covers and so on, there will be an enormous challenge to detect and track moving object correctly.In this thesis, the development state of robot has been introduced first. The robot systematic design, including the composition and operating principle of the system, has been proposed. It has discussed several moving object detection methods and achieves one kind of frame difference method and the background frame difference method and the Surendra method, proposes an improved Surendra method which enhances the background reconstruction to illumination and perturbation robustness. It has discussed the principle what the template match is. Thesis proposes one method based on the Hausdorff distance template match with the Chamfer distance transform. Last, it has discussed the Kalman Filter method on the moving object. Structure of model has been constructed, and the simulation has been carried on. The algorithm has been designed, which can get the moving object's location's evaluation and enhances the match speed.