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Fuzzy Rule-Based Model to Train Videos in Video Surveillance System

A. Manju1, A. Revathi2, M. Arivukarasi1, S. Hariharan3, V. Umarani4, Shih-Yu Chen5,*, Jin Wang6

1 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India
2 Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
3 Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India
4 Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, Tamilnadu, India
5 Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Taiwan
6 School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410004, China

* Corresponding Author: Shih-Yu Chen. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 905-920.


With the proliferation of the internet, big data continues to grow exponentially, and video has become the largest source. Video big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their semantic association. Two evaluation methods including clustering and retrieval are performed to measure the semantic relatedness between images accurately and robustly. A Fuzzy Rule-Based Model for Semantic Content Extraction is designed which performs classification with fuzzy rules. The features extracted are trained with the neural network where each network contains several layers among them each layer of neurons is dedicated to measuring the weight towards different semantic events. Each neuron measures its weight according to different features like shape, size, direction, speed, and other features. The object is identified by subtracting the background features and trained to detect based on the features like size, shape, and direction. The weight measurement is performed according to the fuzzy rules and based on the weight measures. These frameworks enhance the video analytics feature and help in video surveillance systems with better accuracy and precision.


Cite This Article

A. Manju, A. Revathi, M. Arivukarasi, S. Hariharan, V. Umarani et al., "Fuzzy rule-based model to train videos in video surveillance system," Intelligent Automation & Soft Computing, vol. 37, no.1, pp. 905–920, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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