Key Frame Extraction and Foreground Modelling Using K-Means Clustering

Nasreen, Azra; Roy, Kaushik; Roy, Kunal; Shobha, G

doi:10.1109/cicsyn.2015.34

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…The initial phase involves preprocessing, commencing with transforming input films into a series of frames. Next, the keyframe selection method is applied to video frames using the Cosine Similarity (CS) algorithm [25] by measuring the similarity between two frames (current frame and prior keyframe). Next, compare the acquired result with the similarity threshold value to ascertain if the frame qualifies as a keyframe.…”

Section: Proposed Offline Methods To Generate Anomaly Detection Modelmentioning

confidence: 99%

“…Keyframes are frames in a video that provide a comprehensive summary of the entire video and can be extracted to remove nearby repetitive frames effectively. Keyframe selection is the process of choosing frames that include new information [25]. The keyframe selection process aims to summarize the video by eliminating redundant adjacent frames to decrease the amount of information to be processed and reduce computational complexity [26].…”

Section: A Pre-processing Stagementioning

confidence: 99%

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Video-based Domain Generalization for Abnormal Event and Behavior Detection

Jarraya,

Almazroey

2024

IJACSA

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Surveillance cameras have been widely deployed in public and private areas in recent years to enhance security and ensure public safety, necessitating the monitoring of unforeseen incidents and behaviors. An intelligent automated system is essential for detecting anomalies in video scenes to save the time and cost associated with manual detection by laborers monitoring displays. This study introduces a deep learning method to identify abnormal events and behaviors in surveillance footage of crowded areas, utilizing a scene-based domain generalization strategy. By utilizing the keyframe selection approach, keyframes containing relevant information are extracted from video frames. The chosen keyframes are utilized to create a spatio-temporal entropy template that reflects the motion area. The acquired template is then fed into the pre-trained AlexNet network to extract high-level features. The study utilizes the Relieff feature selection approach to choose suitable features, which are then served as input to Support Vector Machine (SVM) classifier. The model is assessed using six available datasets and two datasets built in this research, containing videos of normal and abnormal events and behaviors. The study found that the proposed method, utilizing domain generalization, surpassed state-of-arts methods in terms of detection accuracy, achieving a range from 87.5% to 100%. It also demonstrated the model's effectiveness in detecting anomalies from various domains with an accuracy rate of 97.13%.

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Section: Proposed Offline Methods To Generate Anomaly Detection Modelmentioning

confidence: 99%

Section: A Pre-processing Stagementioning

confidence: 99%

Video-based Domain Generalization for Abnormal Event and Behavior Detection

Jarraya,

Almazroey

2024

IJACSA

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“…In this method colour feature information is employed, shots are clustered into sub-shots, and the frame that has the largest entropy is selected as a KF from each class. In [30] presented a method for extracting KFs and isolating the foreground. They employed a k-means algorithm along with means-squared error.…”

Section: : Global Comparison Between Framesmentioning

confidence: 99%

Video Structure Analysis: A survey

T. Al-Fatlawi,

M.hamza,

L. Albukhnefis

2023

J. Al-Qadisiyah Comp. Sci. Math.

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Due to the great and rapid development in the electronic field, the spread of the Internet, and the diversity of social media, the spread and transmission of data have become an important matter in different areas of life. The video is one of the most important of this information, which includes a lot of information that requires storing and retrieving a large database. Therefore, video structure analysis is the basis for facilitating the video search process based on its contents, indexing, and retrieval. In this research, we will present a survey of the video structure analysis process, the basic concepts and all related processing steps, the procedures used in each method, and the most prominent works used in each step.

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“…The set of key frames is created with frames that have the closest distance from the center of the cluster. In [21,22] fuzzy K-means-and fuzzy C-means-based methods for the key frame selection are presented. The clusters are generated based on the different features like motion sequences and the distance matrix score.…”

Section: Cluster-based Key Frame Extractionmentioning

confidence: 99%

Deep Learning Approach to Key Frame Detection in Human Action Videos

Gawande¹,

Hajari²,

Golhar³

2020

Recent Trends in Computational Intelligence

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A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of constituting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.

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Key Frame Extraction and Foreground Modelling Using K-Means Clustering

Cited by 11 publications

References 5 publications

Video-based Domain Generalization for Abnormal Event and Behavior Detection

Video-based Domain Generalization for Abnormal Event and Behavior Detection

Video Structure Analysis: A survey

Deep Learning Approach to Key Frame Detection in Human Action Videos

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