Camera Localization Based on Belief Clustering

Abstract

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.

CMLocate: A cross‐modal automatic visual geo‐localization framework for a natural environment without GNSS information

“…Tzeng's [33], Hammoud's [60], and Chen et al's [61] methods all conducted experiments in extremely large areas and achieved only a rough estimate of positions with large localization errors. Grelsson's [37], Chiodini's [38], and Fukuda's [62] methods initially used GNSS information to obtain the original position and subsequently used skylines to achieve precise localizations.…”