Inhalt des Dokuments
Background Subtraction
Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they
still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker distributions. Based on the results of the Split and Merge EM algorithm, we propose a solution to this problem. Therefore, we define an appropriate splitting operation and the corresponding criterion for the selection of candidate modes, for the case of background subtraction. We further enhance the performance of the proposed model by incorporating information from a static objects detector algorithm.
Related Publications
2014
- Rubén Heras Evangelio, Michael Pätzold, Ivo Keller, Thomas Sikora
Adaptively Splitted GMM with Feedback Improvement for the Task of Background Subtraction
IEEE Transactions on Information Forensics & Security, vol. 9, no. 5, May 2014, pp. 863-874
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2012
- Rubén Heras Evangelio, Michael Pätzold, Thomas Sikora
Splitting Gaussians in Mixture Models
9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Beijing, China, 18.09.2012 - 21.09.2012
ISBN: 978-1-4673-2499-1
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2011
- Rubén Heras Evangelio, Thomas Sikora
Complementary Background Models for the Detection of Static and Moving Objects in Crowded Environments
8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), volume 2011, Klagenfurt, Austria, 30.08.2011 - 02.09.2011, pp. 71-76
E-ISBN : 978-1-4577-0843-5 Print ISBN: 978-1-4577-0844-2
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