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L. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2008.924285, Vol. 17, no. 7, pagg. 1168-1177, July 2008
Usage:
where
<SeqName>: sequence name (complete path), not including frame numbers. Image sequences consist of binary PPM image frames with consecutive numbers, named in the following form The number of digits for must be the same for all sequence frames (e.">
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L. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2008.924285, Vol. 17, no. 7, pagg. 1168-1177, July 2008
Usage:
where
<SeqName>: sequence name (complete path), not including frame numbers. Image sequences consist of binary PPM image frames with consecutive numbers, named in the following form The number of digits for must be the same for all sequence frames (e."><meta property="og:image" content="/img/logo.png">
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<h1>Moving Object Detection Software</h1>
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<p>Implementation of SOBS algorithm as described in:</p>
<p>L. Maddalena, A. Petrosino, <a href="https://ieeexplore.ieee.org/document/4527178/">A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications</a>, <strong>IEEE Transactions on Image Processing</strong>, DOI: 10.1109/TIP.2008.924285, Vol. 17, no. 7, pagg. 1168-1177, July 2008</p>
<p><strong>Usage</strong>:</p>
<!-- raw HTML omitted -->
<p>where</p>
<ul>
<li><code><SeqName></code>: sequence name (complete path), not including frame numbers. Image sequences consist of binary PPM image frames with consecutive numbers, named in the following form</li>
</ul>
<!-- raw HTML omitted -->
<p>The number of digits for <!-- raw HTML omitted --> must be the same for all sequence frames (e.g. a sequence with 120 frames must be numbered from 1001 to 1120, and not from 1 to 120)</p>
<ul>
<li>
<p><code><#FirstFrame>, <#LastFrame></code>: number of first and last sequence frame to be considered.</p>
</li>
<li>
<p>[parameters]: optional, including:</p>
<ul>
<li>
<p>-n #: (square root of) number of weight vectors for each pixel. Default 3</p>
</li>
<li>
<p>-K #: Number of initial frames for calibration. Default 200</p>
</li>
<li>
<p>-e1 #: Distance threshold e1 for calibration phase (eqn. (2)). Default 0.1</p>
</li>
<li>
<p>-e2 #: Distance threshold e2 for online phase (eqn. (2)). Default 0.03</p>
</li>
<li>
<p>-c1 #: Learning rate c1 for calibration phase (eqn. (4)). Default 1.0</p>
</li>
<li>
<p>-c2 #: Learning rate c2 for online phase (eqn. (4)). Default 0.05</p>
</li>
<li>
<p>-g #: Value for g in eqn. (5). Default 0.7</p>
</li>
<li>
<p>-b #: Value for b in eqn. (5). Default 1.0</p>
</li>
<li>
<p>-tS #: Value for tS in eqn. (5). Default 0.1</p>
</li>
<li>
<p>-tH #: Value for tH in eqn. (5). Default 10.0</p>
</li>
<li>
<p>-s: To apply shadow removal. Default: no shadow removal</p>
</li>
<li>
<p>-m: To save background model images. Default: do not save</p>
</li>
<li>
<p>-l: To save just last detection mask. Default: save all</p>
</li>
<li>
<p><code><!--[if !supportEmptyParas]--> <!--[endif]--></code></p>
</li>
</ul>
</li>
</ul>
<p><strong>Example of use:</strong></p>
<!-- raw HTML omitted -->
<p>where sequence WavingTrees, coming from sequences adopted in K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: principles and practice of background maintenance,” in Proc. 7th IEEE Conf. Computer Vision, 1999, vol. 1, pp. 255–261, has been saved in binary PPM image files named:</p>
<!-- raw HTML omitted -->
<p>and stored in directory c:/Sequences/WavingTrees.
<br>
\</p>
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<td>Download the SOBS software <a href="/rar/SOBS.rar">here</a>.</td>
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