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<h2><a href="/code/modse/">Moving Object Detection Sequences</a></h2>
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Sequence MSA
Sequence MSA is a home-made indoor sequence manually labeled, consisting of 528 frames of 320*240 spatial resolution, acquired at a frequency of 30 fps. The scene consists of a university hall, where a man comes in, leaves a bag on the floor, and then comes out. It represents an example of easy sequence, in that lighting conditions are quite stable and moving objects are well contrasted with the background (there is no camouflage); however, strong shadows cast by moving objects can be observed in the entire sequence.
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<h2><a href="/code/mods/">Moving Object Detection Software</a></h2>
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Implementation of SOBS algorithm as described in:
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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<h2><a href="/code/sc-sobs/">Moving Object Detection Software: SC-SOBS</a></h2>
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This page has been created in order to distribute a prototype software implementing the Spatially Coherent Self-Organizing Background Subtraction (SC-SOBS) algorithm presented in
L. Maddalena, A. Petrosino, The SOBS Algorithm: What Are the Limits?, IEEE Workshop on Change Detection, at CVPR 2012
If you use the software, please cite the above mentioned paper.
Click here to download the Windows executable (WinZip compressed) together with the needed OpenCV .dll's. Basic usage for Change Detection Competition: Unzip the SC-SOBS.
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<h2><a href="/code/rose/">ROSE (Rough Outlier Set Extraction)</a></h2>
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ROSE is a machine learning algorithm that implements outlier detection of an unlabeled spatiotemporal dataset using a rough set approach. It also provides a representative subset of the original data, describing the same structure, with which it is possible to detect the same outliers, named kernel set.
The paper that reports all the details and should be cited when the code is used is
Albanese A, Sankar K P, Petrosino A.
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