Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos
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...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/4/113 |
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| author | Theodora Kyprianidi Effrosyni Doutsi Panagiotis Tsakalides |
| author_facet | Theodora Kyprianidi Effrosyni Doutsi Panagiotis Tsakalides |
| author_sort | Theodora Kyprianidi |
| collection | DOAJ |
| description | 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 demonstrates effectiveness in detecting abrupt scene changes, achieving results comparable to state-of-the-art techniques. We also apply RQA to foreground/background segmentation using the UCF101 and DAVIS datasets, where it accurately distinguishes between foreground motion and static background regions. Through the examination of heatmaps based on the embedding dimension and Recurrence Plots (RPs), we show that RQA provides precise segmentation, with RPs offering clearer delineation of foreground objects. Our findings indicate that RQA is a promising, flexible, and computationally efficient approach to video analysis, with potential applications across various domains requiring dynamic video processing. |
| format | Article |
| id | doaj-art-61fa1d71f23a4782b58f370ccbf4a19b |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-61fa1d71f23a4782b58f370ccbf4a19b2025-08-20T03:13:47ZengMDPI AGJournal of Imaging2313-433X2025-04-0111411310.3390/jimaging11040113Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in VideosTheodora Kyprianidi0Effrosyni Doutsi1Panagiotis Tsakalides2Foundation for Research and Technology—Hellas, 70013 Heraklion, GreeceFoundation for Research and Technology—Hellas, 70013 Heraklion, GreeceFoundation for Research and Technology—Hellas, 70013 Heraklion, GreeceThis 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 demonstrates effectiveness in detecting abrupt scene changes, achieving results comparable to state-of-the-art techniques. We also apply RQA to foreground/background segmentation using the UCF101 and DAVIS datasets, where it accurately distinguishes between foreground motion and static background regions. Through the examination of heatmaps based on the embedding dimension and Recurrence Plots (RPs), we show that RQA provides precise segmentation, with RPs offering clearer delineation of foreground objects. Our findings indicate that RQA is a promising, flexible, and computationally efficient approach to video analysis, with potential applications across various domains requiring dynamic video processing.https://www.mdpi.com/2313-433X/11/4/113recurrence quantification analysis (RQA)dynamic video processingscene change detectionforeground/background segmentationvideo analysis |
| spellingShingle | Theodora Kyprianidi Effrosyni Doutsi Panagiotis Tsakalides Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos Journal of Imaging recurrence quantification analysis (RQA) dynamic video processing scene change detection foreground/background segmentation video analysis |
| title | Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos |
| title_full | Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos |
| title_fullStr | Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos |
| title_full_unstemmed | Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos |
| title_short | Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos |
| title_sort | recurrence quantification analysis for scene change detection and foreground background segmentation in videos |
| topic | recurrence quantification analysis (RQA) dynamic video processing scene change detection foreground/background segmentation video analysis |
| url | https://www.mdpi.com/2313-433X/11/4/113 |
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