Anti-interference small target tracking from infrared dual waveband imagery

For infrared small target detection and tracking, the challenge mainly contains dim targets, cluttered background, and infrared decoys. In the face of the above difficulties, traditional tracking algorithms focus on utilizing information of a single waveband is difficult to achieve good performance. In this paper, a tracking algorithm, which combines the Kernelized Correlation Filter (KCF) with a detection model, is presented to cope with these challenges on dual waveband imagery. Firstly, the Side Window Box Filter (SWBF) is utilized to suppress the background edge in the wide-band image, which contains much more detail than that in narrow band image. Therefore, several filters are obtained in the first wide-band image processed by the SWBF. Subsequently, fused features for the KCF algorithm can be computed via convolution operation. In order to solve the migration problem referring to decoys, a detection model based on multi-frame association and image correlation is introduced to refind the target. Finally, experimental results demonstrate that the presented algorithm outperforms several relative algorithms in the accuracy and robustness. Besides, the multi-source information leads to improve the performance of tracking methods.

Automated summary

This paper presents a tracking algorithm that combines KCF with a detection model to tackle challenges in infrared dual band imagery. By using SWBF to suppress background edge, computing fused features and introducing a detection model, it solves migration problem and improves accuracy and robustness.