Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 21, Issue 3, Pages 225-246Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S012906571100281X
Keywords
Probabilistic self-organising maps; unsupervised learning; computer vision; background modeling; object detection
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Funding
- Ministry of Science and Innovation of Spain [TIN2010-15351]
- Autonomous Government of Andalusia (Spain) [P06-TIC-01615, P07-TIC-02800]
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Background modeling and foreground detection are key parts of any computer vision system. These problems have been addressed in literature with several probabilistic approaches based on mixture models. Here we propose a new kind of probabilistic background models which is based on probabilistic self-organising maps. This way, the background pixels are modeled with more flexibility. On the other hand, a statistical correlation measure is used to test the similarity among nearby pixels, so as to enhance the detection performance by providing a feedback to the process. Several well known benchmark videos have been used to assess the relative performance of our proposal with respect to traditional neural and non neural based methods, with favourable results, both qualitatively and quantitatively. A statistical analysis of the differences among methods demonstrates that our method is significantly better than its competitors. This way, a strong alternative to classical methods is presented.
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