Real-time monocular vision-based object tracking with object distance and motion estimation

This paper presents a real-time vision-based object tracking system consisting of a camera on a 2-DOF manipulator which, for example, can be a PT camera. The main novelties of the proposed tracking system include the ability to i) reduce the image processing load by relying on the object position as the only feature of the images acquired from a camera, and ii) estimate the distance and motion of the object without the need for an active rangefinder. The object tracking system is capable of controlling a manipulator using a feedback system based on the object position. The control rule of the feedback system is to minimize the distance between the object position in the camera image and the center point of the image. The proposed method can be readily adopted in dynamic environments, and achieve better tracking accuracies and efficiencies than the traditional methods. The formulation of the distance and motion estimation is presented in the paper. The efficiency and robustness of the proposed method in dealing with noise is verified by simulation using a PT camera.