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Because of the importance of small target detection in infrared images, many studies have been carried out in thisarea. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small movingtargets even in cases of target disappearance and appearance in serial infrared images in an environment with manynoises. Difference images, which highlight the background images estimated with a background estimation filterfrom the original images, have a relatively very bright value, which becomes a candidate target area. Multiple targettracking consists of a Kalman filter section (target position prediction) and candidate target classification section(target selection). The system removes error detection from the detection results of candidate targets in still imagesand associates targets in serial images. The final target detection locations were revised with the mean shift algorithmto have comparatively low tracking location errors and allow for continuous tracking with standard model updating.In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalmanfilter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errorsand ensured stable tracking with no tracking location diffusion

Small Target Detecting and Tracking Using Mean Shifter Guided Kalman Filter