• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

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

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


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


  1. Jiwan D, SC, Hoi H, Pengchengwu J, Zhu Y, Zhang J, et al. Deep learning for content-based image retrieval: A comprehensive study. Proceedings of the ACM International Conference on Multimedia. 2014;p. 157–166. Available from: https://doi.org/10.1145/2647868.2654948
  2. Nagarkar P, Candan KS. An index structure for efficient execution of set queries in high dimensional spaces. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018;p. 477–486. Available from: https://doi.org/10.1145/3269206.3271691
  3. Zhang Y, Li X, Zhang Z, Wu F, Zhao L. Deep learning driven block wise moving object detection with binary scene modelling. Neurocomputing. 2015;168:454–463. Available from: https://doi.org/10.1016/j.neucom.2015.05.082
  4. Licheng J, Ruohan Z, Fang L, Shuyuan Y, Biao H, Lingling L, et al. New Generation Deep Learning for Video Object Detection: A Survey. IEEE Transactions on Neural Networks and Learning Systems. 33:3195–3215. Available from: https://doi.org/10.1109/TNNLS.2021.3053249
  5. Kim B, Lee J. A Video-Based Fire Detection Using Deep Learning Models. Applied Sciences. 2019;9(14):2862. Available from: https://doi.org/10.3390/app9142862
  6. Boukabous M, Azizi M. Image and video-based crime prediction using object detection and deep learning. Bulletin of Electrical Engineering and Informatics. 12(3):1630–1638. Available from: https://doi.org/10.11591/eei.v12i3.5157
  7. Kumar K, Kumar K, Gupta CLP. Object Detection in Video Frames using Deep Learning. International Journal of Computer Applications. 2022;183(51):33–39. Available from: https://www.ijcaonline.org/archives/volume183/number51/kumar-2022-ijca-921930.pdf
  8. Zheng W, Wang K, Wang FY. A novel background subtraction algorithm based on parallel vision and Bayesian GANs. Neurocomputing. 2020;394:178–200. Available from: https://doi.org/10.1016/j.neucom.2019.04.088
  9. Lee DH. CNN-based single object detection and tracking in videos and its application to drone detection. Multimedia Tools and Applications. 2021;80(26-27):34237–34248. Available from: https://doi.org/10.1007/s11042-020-09924-0
  10. Yoo JY, Ko JH. Acceleration of DNN-Based Video Object Detection Using Temporal Dependency of the Object Size. 2021 International Conference on Information and Communication Technology Convergence (ICTC). 2021;2021:1182–1184. Available from: https://doi.org/10.1109/ICTC52510.2021.9620830
  11. Zhu H, Wei H, Li B, Yuan X, Kehtarnavaz N. Real-Time Moving Object Detection in High-Resolution Video Sensing. Sensors. 20(12):3591. Available from: https://doi.org/10.3390/s20123591
  12. Yadav S, Chaware SM. Video Object Detection with an Improved Classification Approach. In: B, AM., eds. Data Management, Analytics and Innovation. (Vol. 662, pp. 511-523) Springer Nature Singapore. 2023.
  13. Tseng CH, Hsieh CC, Jwo DJ, Wu JH, Sheu RK, Chen LC. Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation. Journal of Sensors. 2021;2021:1–12. Available from: https://doi.org/10.1155/2021/9566628
  14. Zhao X, Wang G, He Z, Jiang H. A survey of moving object detection methods: A practical perspective. Neurocomputing. 2022;503:28–48. Available from: https://doi.org/10.1016/j.neucom.2022.06.104
  15. Salama A, Smarandache F, Yasser I. Neutrosophic Knowledge. (Vol. 1) 2020.
  16. Hwan PC. Neutrosophic ideal of Subtraction Algebras. Neutrosophic Sets and Systems. 24:36–45. Available from: https://digitalrepository.unm.edu/nss_journal/vol24/iss1/5/
  17. Nandhini TJ, Thinakaran K. CNN Based Moving Object Detection from Surveillance Video in Comparison with GMM. 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). 2022;2022:1–6. Available from: https://doi.org/10.1109/ICDSAAI55433.2022.10028909
  18. Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A. Grasshopper Optimization Algorithm: Theory, Variants, and Applications. IEEE Access. 2021;9:50001–50024. Available from: https://doi.org/10.1109/ACCESS.2021.3067597


© 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)


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