An Evolutionary Framework for Robust Abrupt Transition Detection in Video Sequences

Accurate detection of abrupt transitions, or hard cuts, plays a vital role in video analysis tasks such as indexing, summarization, and scene segmentation. However, traditional fixed-threshold methods often struggle in dynamic conditions due to their sensitivity to noise, motion, and lighting variations. To address this, we propose a novel approach that employs Generalized Normal Distribution Optimization (GNDO) to adaptively determine an optimal threshold for identifying abrupt scene changes. The method begins by extracting grayscale frames and calculating pixel-wise differences between consecutive frames, forming the basis for transition analysis. GNDO then evolves a population of candidate thresholds using a balance of exploration and exploitation to maximize detection accuracy while minimizing false positives. A margin-based post-processing step further refines the output by filtering out weak or insignificant variations. Experimental evaluations on diverse video datasets demonstrate that the proposed GNDO-driven strategy significantly enhances abrupt transition detection performance compared to conventional techniques. The adaptive thresholding mechanism and evolutionary learning not only improve robustness but also eliminate the need for manual parameter tuning, making the method highly effective with average recall, precision, and F1 score, respectively, 98.1%, 96.3%, and 97.1% for real-world video processing applications.