Unsupervised video summarization framework using keyframe extraction and video skimming

Jadon, Shruti; Jasim, Mahmood

doi:10.1109/iccca49541.2020.9250764

Cited by 52 publications

(18 citation statements)

References 14 publications

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“…Hence the first step of Algorithm 1 is to uniform sample the input video in a fixed interval to eliminate the redundant frames. In video summary, the algorithm still can summarize the major information of the video even the FPS is lowered to 5 [55]. Next, we get the feature set from the sampled frames using the pre-trained models.…”

Section: The Proposed Methods For Video Anomaly Explanationmentioning

confidence: 99%

An explainable and efficient deep learning framework for video anomaly detection

Shao

Tunc

et al. 2021

Cluster Comput

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Section: The Proposed Methods For Video Anomaly Explanationmentioning

confidence: 99%

An explainable and efficient deep learning framework for video anomaly detection

Shao

Tunc

et al. 2021

Cluster Comput

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“…In such orientation, the unsupervised video summarization is considered as a key frame selection problem. Shruti et al extracted deep features using CNN and then they applied clustering algorithms to extract interesting keyframes [81]. Abhimanyu el al.…”

Section: Storytellingmentioning

confidence: 99%

Overview of Lifelogging: Current Challenges and Advances

et al. 2021

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Lifelogging is the process of digital tracking of person's daily experiences in varying amounts of details, for a variety of purposes. In recent years, lifelogging has become an increasingly popular area of research due to the growing demands from many applications, such as video surveillance, entertainment, healthcare systems, and intelligent environments. Furthermore, the advancements in devices technology offer the promise to record and store large volumes of personal data in a very cheap manner, using an inexpensive tool. However, the rapid access to this huge deluge of unlabeled and unstructured data and automatically processing it to recognize everyday experiences, still present major challenges. A large number of research have been conducted in recent years to cover different lifelogging aspects, but there is still a lack of studies that provide a comprehensive survey of the available literature, and most of the existing lifelogging surveys generally focus on only one aspect. This review highlights the advances of stateof-the-art in lifelogging from different angles, including its research history, current applications, activity recognition techniques, moment retrieval, storytelling, privacy and security issues, as well as challenges and future research trends.

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“…Several recent approaches to shot boundary detection and keyframe extraction in the literature are based on SIFT [37,38,44] and SURF [39,40,45]. These methods are employed to extract key points and descriptors from a video sequence.…”

Section: ) Local Descriptorsmentioning

confidence: 99%

Static video summarization approach using Binary Robust Invariant Scalable Keypoints

AboElenain¹,

Amin²,

Zarif³

2021

IJCI. International Journal of Computers and Information

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The constant demand and generation of digital video information have recently resulted in an increase in the growth of digital video content. Due to the rapid browsing of large amounts of data, content retrieval and indexing of video require an effective and advanced analysis technique. For quickly browsing, indexing, and accessing massive video archives, video summarizing approaches have been proposed. This research presents a new binary descriptor-based method for video summarization. The proposed method extracts key points and descriptors using a Binary Robust Invariant Scalable Key point (BRISK). For matching the binary descriptors between two successive frames, we employ a Brute-force method. And keyframes are extracted from each shot as the middle frame. Experiments were carried out using open video project data sets containing videos of various genres. The Comparison of user summaries (CUS) evaluation metric is used to assess the proposed method by calculating the accuracy and error rates and comparing it to other methods. As demonstrated by the experimental results, the proposed method gives good results when compared with other methods.

show abstract

Unsupervised video summarization framework using keyframe extraction and video skimming

Cited by 52 publications

References 14 publications

An explainable and efficient deep learning framework for video anomaly detection

An explainable and efficient deep learning framework for video anomaly detection

Overview of Lifelogging: Current Challenges and Advances

Static video summarization approach using Binary Robust Invariant Scalable Keypoints

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