The best of Kalman-filter-based frameworks reported in the literature for rigid object tracking work well only if the object motions are smooth (which allows for tight uncertainty bounds to be used for where to look for the object features to be tracked). In this contribution, we present a new Kalman-filter-based framework that carries out fast and accurate rigid object tracking even when the object motions are large and jerky. The new framework has several novel features, the most significant of which is as follows: the traditional backtracking consists of undoing one-at-a-time the model-to-scene matchings as the pose-acceptance criterion is violated. In our new framework, once a violation of the pose-acceptance criterion is detected, we seek the best largest subset of the candidate scene features that fulfill the criterion, and then continue the search until all the model features have been paired up with their scene correspondents (while, of course, allowing for nil-mapping for some of the model features). With the new backtracking framework, our Kalman filter is able to update on a real-time basis the pose of a typical industrial 3-D object moving at the rate of approximately 5 cm/s (typical for automobile assembly lines) using off-the-shelf PC hardware. Pose updating occurs at the rate of 7 frames per second and is immune to large jerks introduced manually as the object is in motion. The objects are tracked with an average translational accuracy of 4.8 mm and the average rotational accuracy of 0.27$^\circ$.

This paper presents an efficient person tracking algorithm for a vision-based mobile robot using two independently moving cameras each of which is mounted on its own pan/tilt unit. Without calibrating these cameras, the goal of our proposed method is to estimate the distance to a target appearing in the image sequences captured by the cameras. The main contributions of our approach include: 1) establishing the correspondence between the control inputs to the pan/tilt units and the pixel displacement in the image plane without using the intrinsic parameters of the cameras; and 2) derivation of the distance information from the correspondence between the centers of masses of the segmented color-blobs from the left and the right images without stereo camera calibration. Our proposed approach has been successfully tested on a mobile robot for the task of person following in real environments.

This paper presents a robust model-based visual tracking algorithm that can give accurate 3D pose of a rigid object. Our tracking algorithm uses an incremental pose update scheme in a prediction-verification framework. Extended Kalman filter is used to update the pose of a target incrementally to minimize the error between the expected map of the target model and the corresponding gradient edge in the image space. The main contributions of this paper include: 1) A novel approach to how we use the two extremities of straight-lines as features. By taking into account the measurement uncertainties associated with the locations of the extracted extremities of the straight-line, our approach can compare correctly two straight-lines of different lengths. 2) Our use of a test of mean criterion for initiating backtracking and our use of a variable threshold on the output of this criterion that makes nil-matching more effective. We have tested our tracking algorithm with image sequences containing highly cluttered backgrounds. The system successfully tracks objects even when they are highly occluded.

This paper presents a fast tracking algorithm capable of estimating the complete pose (6DOF) of an industrial object by using its circular-shape features. Since the algorithm is part of a real-time visual servoing system designed for assembly of automotive parts on-the-fly, the main constraints in the design of the algorithm were: speed and accuracy. That is: close to frame-rate performance, and error in pose estimation smaller than a few millimeters. The algorithm proposed uses only three model features, and yet it is very accurate and robust. For that reason both constraints were satisfied: the algorithm runs at 60 fps (30 fps for each stereo image) on a PIII-800 MHz computer, and the pose of the object is calculated within an uncertainty of 2.4 mm in translation and 1.5 degree in rotation.