An Optical Flow Field Measurement Algorithm For Flir (Forward-Looking Infrared) Imagery

ABSTRACT In this paper, a technique for measuring the optical flow field from forward-looking infrared (FLIR) imagery is presented. The optical flow field has many applications including scene tracking, registration, moving target identification (MTI) and occlusion detection. However, the lower resolution of FLIR imagery, as well as other considerations such as sensor artifacts and noise, makes it more difficult to accurately measure the flow vectors than is the case with normal visible imagery. We have developed an optical flow field measurement algorithm for FLIR imagery which is a correlation matching technique based on an approach developed by Barnard and Thompson [1], Example results for FLIR images are shown demonstrating the capabilities of the algorithm. 1. The optical flow field (OFF) is a description of the differences between successive images which results from the motion of sensor and/or objects in field view. The OFF is expressed as a field of vectors each describing the instantaneous change in position (velocity) of a real world point on the optic array of the image. The apparent motion in a scene, as summarized by the OFF, is an important source for perception of the spatial environment. The OFF provides good cues for segmentation because it often reveals information concerning the relative depth of objects in the scene, since optical flow and range are both discontinuous at object boundaries. Consequently, an image sequence in which motion information is incorporated may actually be easier to understand than a static image, because the effects of motion can help in the segmentation. In an automatic target recognition system (ATR) the optical flow field can serve as a tool for compensating for the changes in perspective due to a moving sensor platform. It can be used to accurately track the background so that multiple frames can be registered to the same point of reference. In the case where conventional tracking algorithms experience difficulties due to conditions such as rapid looming and rotation, the OFF can be used to accurately maintain several aimpoint locations [2], The OFF can also be used to identify moving targets from a moving sensor platform [3].Two approaches are commonly used to measure the OFF. The first approach is a gradient- based technique which defines a constraint equation relating the gradient of brightness to local flow velocity and then estimates the optical flow under the assumption that it var­ ies smoothly [4,5]. The second approach attempts to measure the optical flow by matching points between successive images [1,3,6,7]. The matching approach has the advantage that it can also be applied to similar, but different, problems such as stereo matching. Addi­ tionally, it lends itself more readily to the measurement of large displacement vectors than gradient-based techniques, which assume continuous variations of intensity over time and therefore must employ hierarchical multi-resolution techniques in order to handle large shifts in position. For many FLIR systems, the high velocity of sensor platform as well as the high velocity of targets in field view make large flow vectors a common occurrence.