Integration of segmentation information and correlation technique for tracking objects in sequences of images

This paper deals with the problem of tracking object points from a sequence of image frames for the purpose of navigation, e.g. guiding a missile onto a target from an on-board camera. Image points may be tracked over a sequence of frames by a conventional correlation algorithm. By tracking several points, the motion parameters of the camera may be estimated. However, the presence of noise and the magnification of image features as the camera approaches the target may cause a tracked point to drift. This paper introduces an improved technique that integrates a multiple point correlation (MPC) tracker with image segmentation information to track image points with greater accuracy. Segmentation is the process of partitioning image pixels into regions, for example, of homogeneous grey values. Based on the resulting region characteristics, our method refines the point positions of the MPC tracker. At each frame the MPC is applied to the original image data. The location of each point given by the MPC tracker is used to identify the region in the segmented image that occupies that position. The tracked point is refined based on measurements made of the region. This paper details two region based refinement techniques used to improve tracking: one uses the centroid and the other uses corner points detected on a region's boundary. Experimental results based on real and synthetic images in both the infrared and visible spectrums shows the potential that this type of integration has for enhancing tracker performance.