Robust regression and outlier detection
Robust regression and outlier detection
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
International Journal of Computer Vision - 1998 Marr Prize
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Comparison of Approaches to Egomotion Computation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Determining the translational speed of a camera from time-varying optical flow
IWCM'04 Proceedings of the 1st international conference on Complex motion
A robust approach for ego-motion estimation using a mobile stereo platform
IWCM'04 Proceedings of the 1st international conference on Complex motion
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A fast and robust algorithm for the detection of independently moving objects by a moving observer by means of investigating optical flow fields is presented. The detection method for independent motion relies on knowledge about the camera motion. Even though inertial sensors provide information about the camera motion, the sensor data does not always satisfy the requirements of the proposed detection method. The first part of this paper therefore deals with the enhancement of earlier work [29] by ego-motion refinement. A linearization of the ego-motion estimation problem is presented. Further on a robust enhancement to this approach is given. Since the measurement of optical flow is a computationally expensive operation, it is necessary to restrict the number of flow measurements. The proposed algorithm uses two different ways to determine the positions, where optical flow is calculated. A fraction of the positions is determined by using a sequential Monte Carlo sampling resampling algorithm, while the remaining fraction of the positions is determined by using a random variable, which is distributed according to an initialization distribution. This approach results in a fixed number of optical flow calculations leading to a robust real time detection of independently moving objects on standard consumer PCs.