Improved gaussian mixture model for the task of object tracking

Authors:
Ronan Sicre;Henri Nicolas
Affiliations:
LaBRI, University of Bordeaux, Cours de la libration, Talence, France and MIRANE S.A.S., Cenon, France;LaBRI, University of Bordeaux, Cours de la libration, Talence, France
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Year:
2011

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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.