自动全局上下文感知相关滤波器跟踪算法
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南京信息工程大学

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国家;国家自然科学基金项目(面上项目,重点项目,重大项目)


Correlation Filter Tracing Algorithm Based on Automatic Global Context Awareness
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Nanjing University of Information Science Technology

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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    目标跟踪中,在基于相关滤波器的跟踪算法中引入正则化后有效提高了跟踪效率,但是却花费大量精力调整预定义参数,此外还有目标响应发生在非目标区域会导致跟踪漂移等问题,因此提出了一种基于时间和自动空间正则化的上下文感知相关滤波器算法。首先,在跟踪过程中,利用目标局部响应变化实现自动空间正则化,将自动空间正则化模块加入目标函数,使滤波器专注于目标对象的学习;其次,跟踪器利用目标的全局上下文信息,与自动空间正则化相结合,使滤波器能及时学习到更多与目标有关信息,减少背景对跟踪性能影响;接着,在滤波器中加入时间正则化项,来充分学习目标在相邻帧之间的变化,从而获得更准确的模型样本。实验结果表明,与其他跟踪算法相比,提出的算法在距离精度和成功率方面具备更好的跟踪效果。

    Abstract:

    In the target tracking, the tracking efficiency is effectively improved after introducing regularization in the tracking algorithm based on the correlation filter, but it spends a lot of energy adjusting the predefined parameters. In addition, the target response occurs in non-target areas will lead to tracking drift. Therefore, a context-aware correlation filter algorithm based on time and automatic spatial regularization is proposed. First, during the tracking process, the automatic spatial regularization is realized using the target local response change, adding the automatic space regularization module to the target function to enable the filter to focus on the learning of the target object. Second, the tracker makes full use of the global context information of the target, combined with the automatic spatial regularization, to enable the filter to learn more information about the target in time, and reduce the impact of the background on the tracking performance. Subsequently, we add a temporal awareness to the filter to fully learn the change of targets between adjacent frames to obtain more accurate model samples. Experimental results show that the algorithm has better tracking effect in distance accuracy and success rate when comparing to other tracking algorithms.

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  • 收稿日期:2021-07-30
  • 最后修改日期:2021-10-06
  • 录用日期:2021-11-18
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