Camera Localization Based on Belief Clustering

S. Le Hégarat-Mascle (1, 2, 3) , Huiqin Chen (2, 3) , Emanuel Aldea (2, 3)

1 SATIE - Systèmes et Applications des Technologies de l'Information et de l'Energie
2 Université Paris-Saclay
3 SATIE-MOSS - Méthodes et Outils pour les Signaux et Systèmes

This work deals with epipole estimation related to egocentric camera localization in surveillance and security applications. Matching visual features in the images provides some evidences for various solutions, so that epipole localization can be addressed as a fusion task with a large number of sources including outlier ones. In order to deal with source imprecision and uncertainty, we rely on the belief function theory and a 2D framework suited for our application. In this framework, we address the challenges introduced by a large number of sources with a strategy based on clustering and intra-cluster fusion. The proposed method exhibits more robustness in terms of accuracy and precision when compared on real data with the standard algorithms which provide single solution. Since we provide a Basic Belief Assignment as a result, our strategy is particularly adapted for the prospective combination with additional sources of information. Index Terms-Egocentric camera localization, epipole uncertainty , large number of sources, belief function theory 2D

Domains: Artificial Intelligence [cs.AI]; Computer Vision and Pattern Recognition [cs.CV]

S. Le Hégarat-Mascle, Huiqin Chen, Emanuel Aldea. Camera Localization Based on Belief Clustering. 2020 IEEE 23rd International Conference on Information Fusion (FUSION), Jul 2020, Johannesburg, South Africa. pp.1-9, ⟨10.23919/FUSION45008.2020.9190358⟩. ⟨hal-02939143⟩