This paper describes a system for automatically detecting potential targets (that pop-up or move into view)
and to cue the operator to potential threats. Detection of independently moving targets from a moving ground
vehicle is challenging due to the strong parallax effects caused by the camera motion close to the 3D structure in
the environment. We present a 3D approach for detecting and tracking such independently moving targets with
multiple monocular cameras. In our approach, we first recover the camera position and orientation by employing a
visual odometry method. Next, using multiple consecutive frames with the estimated camera poses, the structure
of the scene at the reference frame is explicitly recovered by a motion stereo approach, and corresponding optical
flow fields between the reference frame and other frames are also estimated. Third, an advanced filter is designed
by combining second order differences between 3D warping and optical flow warping to distinguish the moving
object from parallax regions. We present results of the algorithm on data collected with an eight-camera system
mounted on a vehicle under multiple scenarios that include moving and pop-up targets.
This paper presents an image-processing algorithm for estimating both the egomotion of an outdoor robotic platform and the structure of the surrounding terrain. The algorithm is based on correlation, and is embedded in an iterative, multi-resolution framework. As such, it is suited to outdoor ground-based and underwater scenes. Both single-camera rigs and multiple-camera rigs can be accommodated. The use of multiple synchronized cameras results in more rapid convergence of the iterative approach. We describe how the algorithm operates and gives examples of its application in several robotic domains: Autonomous mobility of outdoor robots and Underwater robots.
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