ENMIM: Energetic Normalized Mutual Information Model for Online Multiple Object Tracking with Unlearned Motions

Abstract : In multiple-object tracking, the lack in prior information limits the association performance. Furthermore, to improve tracking, dynamic models are needed in order to determine the settings of the estimation algorithm. In case of complex motions, the dynamic cannot be learned and the task of tracking becomes difficult. That is why online spatio-temporal motion estimation is of crucial importance. In this paper, we propose a new model for multiple target online tracking: the Energetic Normalized Mutual Information Model (ENMIM). ENMIM combines two algorithms: (i) Quadtree Normalized Mutual Information, QNMI, a recursive partitioning methodology involving a region motion extraction; (ii) an energy minimization approach for data association adapted to the constraint of lack in prior information about motion and based on geometric properties. ENMIM is able to handle typical problems such as large inter-frame displacements, unlearned motions and noisy images with low contrast. The main advantage of ENMIM is its parameterless and its capacity to handle noisy multi-modal images without exploiting any pre-processing step.
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Communication dans un congrès
ACIVS 2007 - 9th International Conference on Advanced Concepts for Intelligent Vision Systems, Aug 2007, Delft, Netherlands. Springer Verlag, 4678, pp.955-967, 2007, Lecture Notes in Computer Science. 〈10.1007/978-3-540-74607-2_87〉
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https://hal.inria.fr/inria-00635919
Contributeur : Nathalie Gaudechoux <>
Soumis le : mercredi 26 octobre 2011 - 12:12:42
Dernière modification le : vendredi 25 mai 2018 - 12:02:03

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Abir El Abed, Séverine Dubuisson, Dominique Béréziat. ENMIM: Energetic Normalized Mutual Information Model for Online Multiple Object Tracking with Unlearned Motions. ACIVS 2007 - 9th International Conference on Advanced Concepts for Intelligent Vision Systems, Aug 2007, Delft, Netherlands. Springer Verlag, 4678, pp.955-967, 2007, Lecture Notes in Computer Science. 〈10.1007/978-3-540-74607-2_87〉. 〈inria-00635919〉

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