Static and Dynamic Video Summarization

Karim, Abdul Amir Abdullah; Sameer, Rafal Ali

doi:10.24996/ijs.2019.60.7.23

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The relation between temporal changes and spatial properties in images, or motion, is a key feature of frame sequences; it demonstrates the dynamics of frames [21]. The presentation of motion data from an image sequence has been referred to as the optical flow estimation; optical flow is a 2D motion map that projects the scene's 3D motion onto the image plane.…”

Section: Estimating Optical Flowmentioning

confidence: 99%

Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy

Abid Hussan Altalbi,

Hameed Shaker,

Ezaldeen Ali

2023

Int. J. Onl. Eng.

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Anomaly detection, which is also referred to as novelty detection or outlier detection, is process of identifying unusual occurrences, observations, or events which considerably differ from the bulk of data and do not fit a predetermined definition of typical behavior. Medicine, cybersecurity, statistics, machine vision, law enforcement, neurology, and financial fraud are just a handful of the industries where anomaly detection is used. In the presented study, an online tool is utilized to identify crowd distortions, which could be brought on by panic. An activity map is produced with the use of numerous frames to show the continuity regarding the flow over time following the global optical flow has been calculated in the quickest time and with the highest precision possible utilizing the Farneback approach to calculate the magnitudes. Utilizing a specific threshold, the oddity in the video will be picked up by the activity map's generation of an entropy. The results indicate that the maximum entropy level for indoor video is <0.16 and the maximum entropy level for outdoor video is >0.45. A threshold of 0.04 is used to determine whether a frame is abnormal or normal.

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Section: Estimating Optical Flowmentioning

confidence: 99%

Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy

Abid Hussan Altalbi,

Hameed Shaker,

Ezaldeen Ali

2023

Int. J. Onl. Eng.

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“…The user may not be interested in the complete video's content, or the user may not have enough time to watch the entire video. People may only want to see the abstraction of the video rather than the entire video [1], [2]. Abstraction is a process that delivers a fast overview of the most important or relevant information within the original content.…”

Section: Introductionmentioning

confidence: 99%

Multimodal video abstraction into a static document using deep learning

Abdulsahib

Abdulmunim

2023

IJECE

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<p><span lang="EN-US">Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments.</span></p>

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“…A video offers 2 key benefits when compared to still images, namely the capacity to retain a constantly varied set of viewpoints on a scene and capacity to record temporal (that is, dynamic) evolution regarding the event. There is no denying that recent advancements in distance sensors have had an impact on machine as well as computer vision applications and research [7]. When looking for RGB images or videos, sensor devices offer detailed information related to the scene view and objects [8].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey

Abid Hussan Altalbi,

Hameed Shaker,

Ezaldeen Ali

2023

Int. J. Onl. Eng.

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Depending on the context of interest, an anomaly is defined differently. In the case when a video event isn't expected to take place in the video, it is seen as anomaly. It can be difficult to describe uncommon events in complicated scenes, but this problem is frequently resolved by using high-dimensional features as well as descriptors. There is a difficulty in creating reliable model to be trained with these descriptors because it needs a huge number of training samples and is computationally complex. Spatiotemporal changes or trajectories are typically represented by features that are extracted. The presented work presents numerous investigations to address the issue of abnormal video detection from crowded video and its methodology. Through the use of low-level features, like global features, local features, and feature features. For the most accurate detection and identification of anomalous behavior in videos, and attempting to compare the various techniques, this work uses a more crowded and difficult dataset and require light weight for diagnosing anomalies in objects through recording and tracking movements as well as extracting features; thus, these features should be strong and differentiate objects. After reviewing previous works, this work noticed that there is more need for accuracy in video modeling and decreased time, and since attempted to work on real-time and outdoor scenes.

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Static and Dynamic Video Summarization

Cited by 3 publications

References 14 publications

Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy

Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy

Multimodal video abstraction into a static document using deep learning

Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey

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