A Novel Crowd-Resilient Visual Localization Algorithm Via Robust Pca Background Extraction

We present a novel egocentric visual localization algorithm for an indoor navigation system, called PERCEPT-V, which is designed to assist the blind and visually impaired users traveling independently in an unfamiliar indoor space. Through the integration of a background extraction module based on Robust Principle Component Analysis (RPCA) into the localization algorithm, we successfully improve the resilience of camera localization to the presence of crowds in the observed scene. Experiments using datasets of videos containing various levels of crowd activity show that the proposed algorithm can increase prominently the reliability of localization performance.