Application of Clustering Techniques for Video Summarization – An Empirical Study

John, Alvina Anna; Nair, Binoy B.; N., Col.) Kumar P.

doi:10.1007/978-3-319-57261-1_49

Cited by 12 publications

(2 citation statements)

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“…The extraction of key frames from videos through the application of clustering algorithms is a prevalent method [39]. Here, frames demonstrating significant similarity are grouped together, with the center of the cluster serving as a key frame for the video.…”

Section: Temporal Segmentation and Key Frame Selection Phasementioning

confidence: 99%

Efficient visual information indexation for supporting actionable intelligence and knowledge generation

Nurye,

Aktas,

García

2023

Artificial Intelligence for Security and Defence Applications

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Visual indexing, or the ability to search and analyze visual media such as images and videos, is important for law enforcement agencies because it can speed up criminal investigations. As more and more visual media is created and shared online, the ability to effectively search and analyze this data becomes increasingly important for investigators to do their job effectively. The major challenges for video captioning include accurately recognizing the objects and activities in the image, understanding their relationships and context, generating natural and descriptive language, and ensuring the captions are relevant and useful. Near real-time processing is also required in order to facilitate agile forensic decision making and prompt triage, hand-over and reduction of the amount of data to be processed by investigators or subsequent processing tools. This paper presents a captioning-driven efficient video analytic which is able to extract accurate descriptions of images and videos files. The proposed approach includes a temporal segmentation technique providing the most relevant frames. Subsequently, an image captioning approach has been specialized to describe visual media related to counter-terrorism and cybercrime for each relevant frame. Our proposed method achieves high consistency and correlation with human summary on SumMe dataset, outperforming previous similar methods.

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Section: Temporal Segmentation and Key Frame Selection Phasementioning

confidence: 99%

Efficient visual information indexation for supporting actionable intelligence and knowledge generation

Nurye,

Aktas,

García

2023

Artificial Intelligence for Security and Defence Applications

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“…Several approaches such as Partitioned Clustering ( Valdés & Martnez, 2007 ), Spectral Clustering ( Damnjanovic et al, 2008 ; Stefanidis et al, 2000 ) and K-Mean clustering ( Amiri & Fathy, 2010 ) have been used for different summarization problems. Many techniques such as High-Density Peak Search ( Wu et al, 2017 ), Self-Organizing Maps, and Gaussian Mean ( John, Nair & Kumar, 2017 ) are proposed by researchers to improve the keyframe selection process further to produce less redundant and qualitative video summaries.…”

Section: Related Workmentioning

confidence: 99%

A video summarization framework based on activity attention modeling using deep features for smart campus surveillance system

Wasim

Ahmed

Ahmad

et al. 2022

PeerJ Computer Science

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Like other business domains, digital monitoring has now become an integral part of almost every academic institution. These surveillance systems cover all the routine activities happening on the campus while producing a massive volume of video data. Selection and searching the desired video segment in such a vast video repository is highly time-consuming. Effective video summarization methods are thus needed for fast navigation and retrieval of video content. This paper introduces a keyframe extraction method to summarize academic activities to produce a short representation of the target video while preserving all the essential activities present in the original video. First, we perform fine-grain activity recognition using a realistic Campus Activities Dataset (CAD) by modeling activity attention scores using a deep CNN model. In the second phase, we use the generated attention scores for each activity category to extract significant video frames. Finally, we evaluate the inter-frame similarity index used to reduce the number of redundant frames and extract only the representative keyframes. The proposed framework is tested on different videos, and the experimental results show the performance of the proposed summarization process.

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An Improved Driver Assistance System for Detection of Lane Departure Under Urban and Highway Driving Conditions

Vats

Nair

2019

Communications in Computer and Information Science

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Application of Clustering Techniques for Video Summarization – An Empirical Study

Cited by 12 publications

References 10 publications

Efficient visual information indexation for supporting actionable intelligence and knowledge generation

Efficient visual information indexation for supporting actionable intelligence and knowledge generation

A video summarization framework based on activity attention modeling using deep features for smart campus surveillance system

An Improved Driver Assistance System for Detection of Lane Departure Under Urban and Highway Driving Conditions

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