Kyprianidi Theodora, Doutsi Effrosyni, Tsakalides Panagiotis
Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.
Computer Science Department, University of Crete, 71500 Heraklion, Greece.
J Imaging. 2025 Apr 8;11(4):113. doi: 10.3390/jimaging11040113.
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.
本文介绍了用于动态视频处理的递归量化分析（RQA）的数学框架，探讨了其在两个主要任务中的应用：场景变化检测和自适应前景/背景分割。递归量化分析（RQA）最初是为时间序列分析而开发的，它研究动态系统内状态的重现情况。当应用于视频流时，RQA通过利用视频帧的时间动态来检测递归模式。这种方法为传统深度学习方法提供了一种计算高效且稳健的替代方案，传统深度学习方法通常需要大量的训练数据和高计算能力。我们的方法在三个带注释的视频数据集上进行了评估：Autoshot、RAI和BBC《地球脉动》，在这些数据集上它在检测突然的场景变化方面表现出有效性，取得了与最先进技术相当的结果。我们还将RQA应用于使用UCF101和DAVIS数据集的前景/背景分割，在该应用中它能准确区分前景运动和静态背景区域。通过基于嵌入维度和递归图（RPs）对热图的检查，我们表明RQA提供了精确的分割，RPs能更清晰地描绘前景物体。我们的研究结果表明，RQA是一种有前途、灵活且计算高效的视频分析方法，在需要动态视频处理的各个领域都有潜在应用。
