Abstract To overcome the shortcomings of low signal-to-noise ratio and less available information of infrared images, as well as the challenges of fast camera motion and partial occlusion, a robust tracker via correlation filter and particle filter is proposed for infrared target. Firstly, to explore the strength of the particle-filter-based tracker, a L p -norm based low-rank sparse tracker is proposed. Then, a robust tracker is proposed by complementing the advantages of both correlation-filter-based and particle-filter-based trackers, which can not only handle the camera motion challenge, but also improve tracking accuracy and robustness. Finally, to address the tracking drift problem and deal with the partial occlusion challenge, an effective template update approach is designed according to different characteristics of correlation-filter-based and particle-filter-based trackers. Experimental results on the VOT-TIR2015 benchmark set demonstrate that the proposed tracker can not only outperform several state-of-the-art trackers in terms of both accuracy and robustness, but also effectively handle the challenges such as camera motion, partial occlusion, size change and motion change.

中文翻译：

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基于相关滤波器和粒子滤波器的红外目标鲁棒跟踪算法

摘要 为了克服红外图像信噪比低、可用信息少的缺点，以及相机运动速度快和部分遮挡的挑战，提出了一种基于相关滤波器和粒子滤波器的红外目标鲁棒跟踪器。首先，为了探索基于粒子滤波器的跟踪器的强度，提出了一种基于 L p 范数的低秩稀疏跟踪器。然后，通过补充基于相关滤波器和基于粒子滤波器的跟踪器的优点，提出了一种鲁棒的跟踪器，它不仅可以处理相机运动挑战，还可以提高跟踪精度和鲁棒性。最后，为了解决跟踪漂移问题并应对部分遮挡挑战，根据基于相关滤波器和基于粒子滤波器的跟踪器的不同特点，设计了一种有效的模板更新方法。在 VOT-TIR2015 基准集上的实验结果表明，所提出的跟踪器不仅在准确性和鲁棒性方面都优于几个最先进的跟踪器，而且可以有效地处理相机运动、部分遮挡、大小等挑战。变化和运动变化。