An Enriched Model of Neutrosophical Fuzzy and Grasshopper Convolutional Neural Network Based Moving Object Detection and Classification to Improve Video Surveillance

S Saravanakumar; M Lingaraj

doi:10.17485/IJST/v16i25.1279

Article

An Enriched Model of Neutrosophical Fuzzy and Grasshopper Convolutional Neural Network Based Moving Object Detection and Classification to Improve Video Surveillance

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i25.1279

Year: 2023, Volume: 16, Issue: 25, Pages: 1877-1887

Original Article

An Enriched Model of Neutrosophical Fuzzy and Grasshopper Convolutional Neural Network Based Moving Object Detection and Classification to Improve Video Surveillance

S Saravanakumar^1*, M Lingaraj²

¹Research Scholar, Research and Development Centre, Bharathiar University, Coimbatore, Tamilnadu, India
²Associate Prof & Head, Dept of CS, Sankara College of Science and Commerce, Coimbatore, Tamilnadu, India

*Corresponding Author
Email: [email protected]

Received Date:26 May 2023, Accepted Date:10 June 2023, Published Date:27 June 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives:To identify a suitable object recognition method for video surveillance systems, especially in traffic monitoring, to track and sense multiple objects and classify them by employing conventional algorithms in order to boost accuracy. Methods: Neutrosophical Background Subtraction (NBS) and Grasshopper Optimized Convolutional Neural Network (GO-CNN) are employed to detect and track the objects in real time. The Neutrosophical Fuzzy Background Subtraction Method (NFBSM) is utilized to segment moving objects from the background in terms of truthful degree, false degree, and in-deterministic degree. CNN-based object tracking techniques, along with the grasshopper optimization algorithm (GO), are used for reliable object detection and hyper-parameter fine tuning. The NFBSM+GO-CNN model extracts the features from the dataset (collected from YOLO-V-3 from Kaggle) and selects a suitable function to perform object detection and classification of moving vehicles with a high accuracy rate. Four main features are used such as colour, texture, motion, and shape. PYTHON is used for implementation and comparative analysis, and the findings are compared with baseline models D-CNN and CNN-MOD. Findings: The proposed NFBSM+GO-CNN method outperforms with 98.2% accuracy, 96.3% improved precision, and 96.8% F-measure, which is comparatively higher than the baseline models in terms of real-time object detection and classification. Novelty: The outcome of NFBSM+GO-CNN clearly shows that the proposed model has the ability to detect the object in real-time and classify the vehicle in road traffic monitoring with a high accuracy rate. The proven results outperform existing models such as D-CNN and CNN-MOD.

Keywords: Object Detection; Classification; Machine Learning; CNN; Optimization; Video Surveillance Systems

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Copyright

© 2023 Saravanakumar & Lingaraj. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)