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A survey on abnormal behavior detection based intelligence information video surveillance system using optimized machine learning methods
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DOI：10.1016/j.engappai.2025.113438.png)
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摘要
En 中文
One of the core topics for research in machine learning (ML) and computer vision (CV) is surveillance video anomaly detection. With the increase in surveillance camera deployment, the ongoing demand for a fully automatic system that can analyze and detect abnormal activities in recorded video has increased tremendously. In this paper, various recent works based on abnormality detection in the Intelligence Information Video Surveillance System (IIVSS) have been reviewed comprehensively, and an in-depth comparative discussion of anomaly detection methods is provided. The robustness features, merits/demerits, and utilization of optimization approaches for abnormal behavior detection using ML and deep learning (DL) techniques, such as Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have been highlighted. Furthermore, a comparative analysis between different existing methods in standard benchmark datasets, such as the University of California, San Diego (UCSD), and the University of Minnesota (UMN), is also provided. Moreover, anomaly detection approaches using ML and DL, summarization of the different learning algorithms, limitation of current solutions, new opportunities, research gaps, and fusion of anomalous behavior detection in existing video analytics system have been critically analyzed.
Keyword:
Machine learning (ML)
Deep learning (DL)
Convolution neural network (CNN)
Recurrent neural network (RNN)
University of California San Diego (UCSD)
University of Minnesota (UMN)
Anomaly detection
Optimization techniques
Behavior techniques
Intelligence information video surveillance
system
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8
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5.8K
被引数:
3.5W
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