Abstract
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.
Highlights
In the field of computer vision, video surveillance is always an important tool in a variety of security applications
We have demonstrated that by using a compressive imaging (CI) system we can detect and track objects in motion with significantly fewer data samples than conventional image methods
A parallel coded aperture imaging array, which is based on a phase-coded 4F system, is used to simulate compressive sensing images
Summary
In the field of computer vision, video surveillance is always an important tool in a variety of security applications. The challenge in video surveillance systems is that the use of conventional imaging approaches in such applications can result in overwhelming data bandwidths. To solve this problem, researchers generally compress those high-resolution video streams by using various data compression algorithms to reduce the overall bandwidth to a more manageable level. In video surveillance systems moving objects occupy only a small part of the full image, and a large portion of any obtained image data is redundant, such as the static background in the field of view that is repeated in every frame. For motion detection algorithms background images are generally assumed to be temporally stationary, whereas moving objects or foreground objects change over time.
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