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Compressed Normalized Block Difference for Object Tracking

机译：用于对象跟踪的压缩归一化块差异

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Feature extraction is very important for robust and real-time tracking. Compressive sensing provided a technical support for real-time feature extraction. However, all existing compressive tracking were based on compressed Haar-like feature, and how to compress many more excellent high-dimensional features is worth researching. In this paper, a novel compressed normalized block difference feature (CNBD) was proposed. For resisting noise effectively in a high-dimensional normalized pixel difference feature (NPD), a normalized block difference feature extends two pixels in the original formula of NPD to two blocks. A CNBD feature can be obtained by compressing a normalized block difference feature based on compressive sensing theory, with the sparse random Gaussian matrix as the measurement matrix. The comparative experiments of 7 trackers on 20 challenging sequences showed that the tracker based on CNBD feature can perform better than other trackers, especially than FCT tracker based on compressed Haar-like feature, in terms of AUC, SR and Precision.
机译：特征提取对于鲁棒和实时跟踪非常重要。压缩感测为实时特征提取提供了技术支持。但是，所有现有的压缩跟踪都基于类似Haar的压缩特征，因此如何压缩许多更出色的高维特征值得研究。本文提出了一种新颖的压缩归一化块差异特征（CNBD）。为了有效抵抗高维归一化像素差异特征（NPD）中的噪声，归一化块差异特征将NPD原始公式中的两个像素扩展到两个块。通过基于稀疏随机高斯矩阵作为测量矩阵，基于压缩感测原理对归一化块差异特征进行压缩，可以获得CNBD特征。在20个具有挑战性的序列上对7个跟踪器进行了对比实验，结果表明，基于CNBD特征的跟踪器在AUC，SR和Precision方面的性能要优于其他跟踪器，尤其是比基于压缩Haar类特征的FCT跟踪器更好。

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