A review on video summarization techniques

• Published in: Engineering applications of artificial intelligence 2023-02, Vol.118, p.105667, Article 105667
• Main Authors: Meena, Preeti, Kumar, Himanshu, Kumar Yadav, Sandeep
• Format: Article
• Language: English
• Subjects: Modality fusion; Multi-modal; Multi-view; Single view; Video summarization
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2022.105667

The exponential growth of technology has resulted in a profusion of advanced imaging devices and eases internet accessibility, leading to an increase in the creation and use of multimedia content. Analyzing representative or meaningful information from such massive data is a time-consuming task that impacts the efficiency of various video processing applications, including video searching, retrieval, indexing, sharing, and many more. In literature, numerous video summarization techniques which extract key-frames or key-shots from the original video to generate a concise yet informative summary have been proposed to address these issues. This paper presents a discussion of the state-of-the-art video summarization techniques along with limitations and challenges. The paper examines summarization techniques in a holistic manner based upon the distinct attributes of evolving video data types on the basis of parameters such as the number of views, dimensions, modality, and content. Such a categorization framework enables us to critically analyze the recent progress, future directions, limitations, datasets, application domains etc., in a better comprehensible manner. [Display omitted] •Enumerate various video summarization techniques.•Examine methods based on the number of views, dimensions, modality, and context.•Highlights the research gap for each classified category.•Analyze and compare the performance of some prominent video summarization techniques.•Highlights the recent progress, challenges, benchmark datasets, and future directions.