Background modeling and target segmentation via modified Kalman filtering

In target detection and tracking applications with imagery data taken from a moving camera platform, it is necessary to segment potential targets in each image frame. This is typically done by preprocessing individual images to exploit some known attribute about the data. Often these methods make many false detections, particularly in the presence of additive noise, and the results thus require significant post-processing. A means of estimating the background in the imagery sequence under the formalism of the Kalman filter is suggested. This background estimate is then used to recast the segmentation problem as one of outlier detection, and the result of segmentation is used to modify the filter update. Ways of making the technique computationally benign are discussed. The technique is used to analyse a simulated image sequence, and the performance is compared to that of a single-frame background-estimation technique. The feasibility of target segmentation via background tracking is thus demonstrated.