Work

Integrating clustering with LSTM diffusion for video anomaly detection

Year: 2025

Type: article

Abstract: Automatic anomaly detection is very important in smart video surveillance because it helps identify unusual events or actions that do not match normal behavior. This study presents a new approach called Color Histo-Sigmoid Fuzzy C-Means (CHSFCM) as the main idea to improve the accuracy of object separation and background modeling. The method has three steps: first, it uses CHSFCM for pre-processing and segmentation to better understand the background. Second, it uses a Deep Neural Network (DNN) to extract features. Third, it uses a Support-based Long Short-Term Memory (SLSTM) network to tell the difference between normal and unusual events. By combining the strong ability to separate different parts of data from CHSFCM with the time-learning power of SLSTM, the system performs very well, achieving an accuracy of 96.25 %, which is better than other methods. This approach gives a reliable and strong way to spot unusual events in real-time video monitoring.

Source: Applied Soft Computing

DOI: 10.1016/j.asoc.2025.114185

Authors: Anil Kumar Gupta, Rupak Sharma, Rudra Pratap Ojha

Institutions: SRM Institute of Science and Technology, Dr. A.P.J. Abdul Kalam Technical University, G.L. Bajaj Institute of Technology and Management Greater Noida

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,730

OpenAlex ID: W7105205945

FWCI:

Cites: 27

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