Infrared Small Target Tracking Based on Super-resolution and Online Detection DSST
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摘要: 红外小目标的相关研究在军事领域的制导、预警和边防间谍无人机检测中极其重要。针对红外小目标的跟踪研究,本文提出了一种基于超分辨率增强与在线检测DSST(Discriminative Scale Space Tracker)的小目标跟踪算法。首先,基于融入红外图像特征的超分辨率重建算法对原始图像进行更新,增强了弱小目标,然后,增强的图像被用作基于在线检测DSST算法的输入,得到响应映射,估计目标位置。实验结果表明,与几种最新算法相比,该算法在准确性方面表现出色。Abstract: Research on infrared small targets is crucial in the areas of military guidance and early warning and detection of border spy UAVs. In this paper, a small target tracking algorithm based on super-resolution enhancement and online detection DSST is proposed for small target tracking research. First, the original image is updated based on the integrated infrared image features of the super-resolution reconstruction algorithm to enhance the dim target. In addition, the enhanced image is used as the input for the online detection DSST algorithm to perform response mapping and estimate the target position. The experimental results show that the accuracy of the proposed algorithm is high compared with those of several new algorithms.
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Key words:
- small target tracking /
- super resolution /
- DSST /
- on-line inspection
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表 1 实验数据集
Table 1. Experimental data set
Sequence name Seq.1 Seq.2 Seq.3 Seq.4 Seq.5 Seq.6 Image size/pixel 256×256 256×256 256×256 256×256 256×256 256×256 Sequence length/frame 429 341 463 30 349 302 Target size/pixel 5×5 6×6 5×6 2×2 6×6 2×2 Noise scale Large Larger Small Large Larger Large Object speed/(pixel/frame) 6 115 9 12 15 12 Target type Airplane Military drone Civilian drone Pixel point Civilian drone Pixel point -
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