Abstract
This paper presents various motion detection methods: temporal averaging (TA), Bayes decision rules (BDR), Gaussian mixture model (GMM), and improved Gaussian mixture model (iGMM). This last model is improved by adapting the number of selected Gaussian, detecting and removing shadows, handling stopped object by locally modifying the updating process. Then we compare these methods on specific cases, such as lighting changes and stopped objects. We further present four tracking methods. Finally, we test the two motion detection methods offering the best results on an object tracking task, in a traffic monitoring context, to evaluate these methods on outdoor sequences.
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© 2011 Springer-Verlag Berlin Heidelberg
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Sicre, R., Nicolas, H. (2011). Improved Gaussian Mixture Model for the Task of Object Tracking. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23678-5_46
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DOI: https://doi.org/10.1007/978-3-642-23678-5_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23677-8
Online ISBN: 978-3-642-23678-5
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