Robust Monocular Detection of Independent Motion by a Moving Observer

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.

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