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These
videos are test results from our method of detecting moving objects in a surveillance
video by using compressive sensing. Compressive sensing is a new mathematical
tool for signal processing that makes it possible to acquire and represent a
signal using far fewer samples than what is required by Nyquist
sampling rate.

In
our method, a surveillance video is either acquired or transformed into
compressive measurements, which is a form of compression to reduce the
bandwidth requirement of transmitting the video in the network. After
transmitted to a processing center, the measurements are used to reconstruct
simultaneously the background and moving objects in the surveillance video. A
main advantage of this method is that the background and moving objects can be
reconstructed by using an amount of data that is far fewer than the total
number of pixels as in the traditional methods.

In
our method, the background of surveillance video is model as a low rank matrix,
and the moving objects are modeled as a sparse matrix. Therefore, the
background and moving objects are reconstructed by using a low rank and sparse
decomposition. The decomposition is performed by processing the compressive
measurements of the surveillance video.

We
apply our method to four test video sequences, and present the results as shown
in the video files. For each video sequence, the result is presented by showing
the original video, reconstructed background and moving objects together. The
original video is shown in the middle of a frame. The reconstructed background
is shown on the left and the moving objects are shown to the right.

We
also note the number of measurements used in the reconstruction in terms of a
percentage as compared to the total number of pixels in a video. 100% means
that the number of measurements used is equal to the total number of pixels in
the video.

**Browse2**: 4% measurements used

**ShopAssistant1Front**: 4% measurements used

**Traffic**: 6.67% measurements used

**Daniel
light**: 10%
measurements used

The
results demonstrate that the background and moving objects can be accurately
reconstructed with only a fractional data as compared to the total number of
pixels in the video.

The
details of the method can be found in the paper:

**SURVEILLANCE
VIDEO PROCESSING USING COMPRESSIVE SENSING, Hong Jiang, Wei Deng and Zuowei
Shen, ***Inverse Problems and Imaging*, Vol 6, No. 2, pp. 201-214, 2012

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