<title>Robust motion constraint technique for detecting moving targets</title>

Abstract

An initial step in goal-oriented dynamic vision is tracking a nonstationary object, or target, and maintaining its position in the center of the field-of-view for detailed analysis. Any image analysis performed by a dynamic vision system must be able to clearly distinguish between the image flow generated by the changing position of the camera and by the movement of potential targets. Many image-based motion analysis techniques are, however, unable to deal effectively with the complexities of dynamic vision because they attempt to calculate true velocities and accurately reconstruct 3D depth information from spatial and temporal gradients. An alternative pattern classification technique has been developed for qualitatively identifying regions in the image plane which most likely correspond to moving targets. This approach is based on the notion that all projected velocities arising from a camera moving through a rigid environment will lie along a line in the local velocity space. Each point on this constraint line maps a circle that represents all corresponding velocities that are parallel to the direction of the spatial gradient. If the camera motion is known, then the gradient-parallel velocity vectors associated with an independently moving target are unlikely to fall in the region arising from the union of all the circles generated by the points along the constraint line. Imprecise or approximate knowledge of the camera motion can be utilized if the projected velocities associated with the constraint line are modeled as radial fuzzy sets with supports in the local velocity space. Homogeneous regions in the image plane that violate these camera velocity constraints become possible fixation points for advanced tracking and detailed scene analysis.