Real-time detection and tracking of infrared small targets based on grid fast density peaks searching and improved KCF

Abstract

Improving the accuracy and meeting the real-time requirement of detection and tracking for infrared small target is always a research hot-spot. In this paper, we propose a target detection approach based on grid-based density peaks searching. First, in view of the small size of infrared target, Top-hat filter is introduced for preprocessing of raw infrared images, to enhance the target region. Then, the image is divided into image blocks by grid division. Compared to the original method of density peaks searching on each pixel, grid-based density peaks searching algorithm improve the detection speed on the basis of ensuring the detection accuracy. Finally, the small candidate target area is detected by the gray-scale area growth method, and the final small targets are segmented through a threshold. For the infrared small targets tracking, the improved correlation filtering tracking algorithm KCF is used. To address the issue of target loss in the KCF tracking process, we incorporate Kalman filtering into KCF. In the case of the target loss, Kalman filtering is adopted to predict the target’s trajectory. The combination of detection and tracking algorithm could significantly improve the accuracy of infrared small targets tracking, while the fusion of detection algorithm and the tracking algorithm of correlation filtering makes the real-time performance of the algorithm better and more efficient. Experimental results show that compared with several currently typical infrared small targets detection and tracking algorithms, the method proposed in this paper has better detection accuracy and speed.