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Data-driven enabled approaches for criteria-based video summarization: a comprehensive survey, taxonomy, and future directions

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Abstract

The exponential growth in the usage of computing technologies in various applications has led to the creation of huge amount of multimedia information such as, video, audio, and text. The enormous amount of video data generated over the past years necessitates the use of video summarization techniques that has become an emerging field of research. These techniques may facilitate quick browsing, indexing and faster sharing of content among various sources. Video summarization has been popular method to generate a short summary of a longer sized video and these approaches may be broadly classified into handcrafted (using features descriptors) or deep learning (DL) based algorithms. In this paper, we expound a comprehensive review of state-of-the-art (SOTA) techniques for video summarization from traditional to modern data-driven approaches. In addition, we proposed a taxonomy for the classification of video summarization methods based on a plenty of criteria. We also present an analysis of evaluation protocols for these approaches using benchmark datasets and performance metrices. We identify and list various research challenges specifically for each sub-category of video summarization. It may be clearly inferred that modern deep learning-based approaches outperformed traditional methods in terms of accuracy with an additional training overhead. Furthermore, most of the handcrafted-based approaches offer limited performance in dynamic video scenario and there exist several inconsistencies such as scaling or rotational variations under different illumination conditions. Besides, our analysis investigates that multi-criteria-based video summarization is an area that requisite further exploration by the research community. This survey may serve as a reference article to the new researchers for carrying out investigations in this active field of computer vision.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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  1. Department of Computer Science and Information Technology, Central University of Jammu, Samba, Jammu and Kashmir, 181143, India

    Ambreen Sabha & Arvind Selwal

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Sabha, A., Selwal, A. Data-driven enabled approaches for criteria-based video summarization: a comprehensive survey, taxonomy, and future directions. Multimed Tools Appl 82, 32635–32709 (2023). https://doi.org/10.1007/s11042-023-14925-w

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