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Infrared Small Target Tracking Algorithm via Segmentation Network and Multi-strategy Fusion

R. Kou, C. Wang, Y. Yu, Z. Peng, F. Huang and Q. Fu, "Infrared Small Target Tracking Algorithm via Segmentation Network and Multi-strategy Fusion," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3286836.

Abstract:To solve the problem of infrared (IR) small target tracking loss or error caused by factors such as scale changes, motion blur, occlusion, etc., this paper proposes a multi-strategy fusion tracking algorithm using an IR small target segmentation network as the detection head, which mainly includes six strategies: target pixel clustering, target feature threshold adjustment, large area search, small area tracking, gate tracking, and coordinate solution. First, candidate targets are obtained through the IR small target segmentation network and pixel clustering strategy. Second, the range of candidate targets is further reduced through threshold adjustment strategy. Then, real-time tracking of IR small targets is achieved through large area search, small area tracking, and wave gate tracking strategies. Finally, the longitude, latitude, and altitude of the tracked target are obtained through coordinate calculation strategy. Both qualitative and quantitative experiments based on real IR small target sequences verify that our algorithm can achieve more satisfactory performances in terms of success rate, precision, and robustness compared with other typical visual trackers. In addition, we have deployed the tracking algorithm proposed in this study on the Orange Pi 5 embedded platform, and the tracking speed meets the real-time requirements.

The download address and password of the datasets (including the label):

Address：https://pan.baidu.com/s/1-NmgTdk8qKu3WozkJYnMrQ

Password：1qaz

Comparison algorithmas:

MOSSE doi: 10.1109/CVPR.2010.5539960

CSK doi: 10.1007/978-3-642-33765-9_50.

ECO doi: 10.1109/CVPR.2017.733.

BACF doi: 10.1109/ICCV.2017.129.

LADCF doi: 10.1109/TIP.2019.2919201.

ARCF doi: 10.1109/ICCV.2019.00298.

Auto Track http://arxiv.org/abs/2003.12949

EFSCF doi: 10.1016/j.neunet.2023.01.003.