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Fast Anomaly Detection in Video Surveillance System Using Robust Spatiotemporal and Deep Learning Methods by Vijay A. Kotkar; V. Sucharita is a Computer Science article available to read on EtoBox.

What is Fast Anomaly Detection in Video Surveillance System Using Robust Spatiotemporal and Deep Learning Methods about?

Since from emergence of deep learning techniques, automatic video surveillance has received researchers' attention. Such deep learning-based methods improve the accuracy but lead to higher computational complexities than the semi-automatic approach. A unique framework is proposed in this study to bridge the gap between automated and semi-automatic operations to save time and boost accuracy. The main advantage of the proposed model is reducing the computational complexity while improving the overall accuracy using the deep learning approach in video anomaly detection applications. The framework consists of keyframe extraction, robust features extraction, automatic features learning, and classification. Extracting keyframes from the input videos reduces the computational burden during features extraction and classification steps. The novel lightweight keyframe extraction algorithm using the histogram and dynamic thresholding technique is proposed in this paper. This research proposes a revolutionary lightweight keyframe extraction approach based on the histogram and dynamic thresholding technique. Efficient motion tracking among frames is a critical research challenge in video anomal

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Author

Vijay A. Kotkar; V. Sucharita

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)