An Efficient Key Frame Extraction from Surveillance Videos for Real-World Anomaly Detection

Mangai, P.; Geetha, M. Kalaiselvi; Kumaravelan, G.

doi:10.1007/978-3-031-12413-6_16

Third International Conference on Image Processing and Capsule Networks

2022

DOI: 10.1007/978-3-031-12413-6_16

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An Efficient Key Frame Extraction from Surveillance Videos for Real-World Anomaly Detection

P. Mangai

M. Kalaiselvi Geetha

G. Kumaravelan

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2023

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Human Action Recognition: Improving the Accuracy of Deep Conv-LSTM Architecture Through Noise Cleaning Prior to Key Frames Selection

GALSTYAN,

HARUTYUNYAN,

NIKOGHOSYAN

2023

Proceedings

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Recognizing human actions has numerous practical applications that can address issues and enhance the effectiveness and living standards across various domains. For solving the human action recognition task from videos, the Deep Conv-LSTM model architecture has been used on the UCF101 dataset. To preprocess the frames, a method has been proposed that integrates two algorithms: Noise Cleaning and Dissimilarity-Based Key Frame Selec¬t¬i¬o¬n (KFS). By employing these algorithms cohesively and effectively, image quality has been enhanced and unwanted data have been eliminated. The Uniform Frame Selection, Dissimilarity-Based KFS, and the proposed algorithm are evaluated, and their performances are compared based on the accuracy and data size reduction. The results show that the Dissimilarity-Based KFS algorithm outperforms the Uniform Frame Selection algorithm in accuracy by 2%, and the proposed method shows a 3% and 5% improvement in accuracy compared to the first two algorithms, respectively. Furthermore, the proposed algorithm reduces the data size by 26%, making it computationally efficient.

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Human Action Recognition: Improving the Accuracy of Deep Conv-LSTM Architecture Through Noise Cleaning Prior to Key Frames Selection

GALSTYAN,

HARUTYUNYAN,

NIKOGHOSYAN

2023

Proceedings

View full text Add to dashboard Cite

show abstract

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An Efficient Key Frame Extraction from Surveillance Videos for Real-World Anomaly Detection

Cited by 1 publication

References 16 publications

Human Action Recognition: Improving the Accuracy of Deep Conv-LSTM Architecture Through Noise Cleaning Prior to Key Frames Selection

Human Action Recognition: Improving the Accuracy of Deep Conv-LSTM Architecture Through Noise Cleaning Prior to Key Frames Selection

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