A coded aperture compressive imaging array and its visual detection and tracking algorithms for surveillance systems.
A coded aperture compressive imaging array and its visual detection and tracking algorithms for surveillance systems.
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一种用于监控系统的编码孔径压缩成像阵列及其视觉检测和跟踪算法
DOI：
10.3390/s121114397
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发表时间：
2012-10-29
期刊：
影响因子：
--
通讯作者：
Wu H
中科院分区：
文献类型：
--
作者：
Chen J;Wang Y;Wu H
本文提出了一种压缩成像系统在广域视频监控系统中的应用。提出了一种并行编码孔径压缩成像系统，以减少对高分辨率编码掩模的要求，并便于存储投影矩阵。利用随机高斯掩模、Toeplitz掩模和二进制相位编码掩模获得压缩传感图像。提出了直接利用压缩采样图像进行运动目标检测和跟踪的算法。在压缩图像空间中应用混合高斯分布对背景图像进行建模，并用于前景检测。对于压缩采样域中的每个运动目标，稀疏地表示由目标模板和噪声模板跨越的压缩特征字典。采用L1优化算法求解模板的稀疏系数。实验结果表明，低维压缩成像表示法足以确定空间运动目标。与随机高斯和Toeplitz相位模板相比，使用随机二进制相位模板的运动检测算法可以得到更好的检测结果。然而，使用随机高斯和Toeplitz相位掩模可以获得高分辨率的重建图像。在没有任何优化的情况下，我们的跟踪算法可以达到比L1跟踪器快10倍的实时速度。
In this paper, we propose an application of a compressive imaging system to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system is proposed to reduce the needed high resolution coded mask requirements and facilitate the storage of the projection matrix. Random Gaussian, Toeplitz and binary phase coded masks are utilized to obtain the compressive sensing images. The corresponding motion targets detection and tracking algorithms directly using the compressive sampling images are developed. A mixture of Gaussian distribution is applied in the compressive image space to model the background image and for foreground detection. For each motion target in the compressive sampling domain, a compressive feature dictionary spanned by target templates and noises templates is sparsely represented. An l1 optimization algorithm is used to solve the sparse coefficient of templates. Experimental results demonstrate that low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz phase mask, motion detection algorithms using a random binary phase mask can yield better detection results. However using random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed image. Our tracking algorithm can achieve a real time speed that is up to 10 times faster than that of the l1 tracker without any optimization.
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DOI：
10.1109/tpami.2011.66
发表时间：
2011-11-01
影响因子：
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作者：
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