Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos.

ABSTRACT: This paper presents the mathematical framework of Recurrence Quantification Analysis (RQA) for dynamic video processing, exploring its applications in two primary tasks: scene change detection and adaptive foreground/background segmentation. Originally developed for time series analysis, Recurrence Quantification Analysis (RQA) examines the recurrence of states within a dynamic system. When applied to video streams, RQA detects recurrent patterns by leveraging the temporal dynamics of video frames. This approach offers a computationally efficient and robust alternative to traditional deep learning methods, which often demand extensive training data and high computational power. Our approach is evaluated on three annotated video datasets: Autoshot, RAI, and BBC Planet Earth, where it demons

SUBMITTER: Kyprianidi T 

PROVIDER: S-EPMC12027938 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature