Abstract
An efficient video shot boundary detection is highly desirable for subsequent semantic video content analysis and retrieval applications. The major challenge of shot boundary detection problem is an appropriate choice of features to handle the illumination variation and motion artifacts of frames while finding the boundary in a shot. In this paper, to improve the efficacy of shot boundary detection in presence of the aforementioned challenges, the strength of the gradient feature is explored to develop a dual detection framework for automatic shot boundary detection. In the first phase, abrupt transition (AT) detection is addressed in presence of illumination variation and motion in the frames of a shot by generating an combined feature through joint histogram of gradient magnitude and gradient orientation features frame. In the second phase, gradual transition detection (GT) detection is applied only on the frames within two AT frames satisfying specific frame distance criteria. Handling both AT and GT by the proposed simple gradient-based framework is the uniqueness of this work. Moreover, the proposed method is fully ubiquitous, independent of the video content and free from any training process. Exhaustive simulations are carried out on different databases to validate the proposed approach. The performance of the proposed feature-based shot boundary framework, in terms of average F1 measure, is 94% for AT detection, 84% for GT detection and 90.01% for overall detection.
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The Sound and Vision video used in this work is provided solely for research purposes through the TREC Video Information Retrieval Evaluation Project Collection.
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Kar, T., Kanungo, P. A gradient based dual detection model for shot boundary detection. Multimed Tools Appl 82, 8489–8506 (2023). https://doi.org/10.1007/s11042-022-13547-y
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DOI: https://doi.org/10.1007/s11042-022-13547-y