Detection of moving targets using approximate knowledge of camera motion

Rapid detection of independently moving objects by a moving camera system is essential for automatic target recognition (ATR). The image analysis performed by the ATR system must be able to clearly distinguish between the image now generated by the changing position of the camera and the movement of potential targets. In this paper, a qualitative motion detection algorithm that can deal with imprecise knowledge of camera movement is described. This algorithm is based on the notion that the true velocity at any point on an image, arising from a camera moving through a rigid environment, will lie on a one-dimensional locus in the v(x) - v(y) velocity space. Each point on this line maps a constraint circle that represents all components of the true velocity that are parallel to the direction of the spatial gray-scale gradient. If the camera motion is known, then an independently moving target can be detected because the corresponding gradient-parallel components of velocity are unlikely to fall in the constraint region arising from the union of all the circles generated by the points along the 1-D locus. The algorithm is made more robust by modelling the projected camera velocities as radial fuzzy sets with supports in the 2-D velocity space. Approximate knowledge of the translational and rotational components of camera motion can be used to define the parameters of the corresponding fuzzy constraint region. In terms of detecting independently moving targets, the algorithm tags the gradient-parallel velocity vectors that violate this fuzzy constraint on camera motion. An estimate of the true velocity is computed only at the pixel locations that violate the constraint. To illustrate this approach, a simulation study involving a translating camera system and an independently moving target is presented.