Journal of Machine and Computing


Multiple Object Detection on Surveillance Videos for Improving Accuracy Using Enhanced Faster R-CNN



Journal of Machine and Computing

Received On : 30 March 2023

Revised On : 22 July 2023

Accepted On : 15 August 2023

Published On : 05 October 2023

Volume 03, Issue 04

Pages : 505-516


Abstract


Computer vision is a dynamic and rapidly evolving field within the broader domain of artificial intelligence. Within surveillance monitoring systems, one of the central tasks is object detection, which involves identifying and localizing objects of interest in video sequences to provide safety and security of the people. Detection of multiple objects is a challenging task in video sequences which interprets less accuracy and false Bounding box regression. In this paper, enhanced faster R-CNN model is proposed and trained to compute regional proposal through Convolutional layers on the different scene of the sequences in term of lighting, motion capture related to spatial analysis. These enhancements could encompass architectural improvements, novel training strategies, or the incorporation of additional data sources to improve the model's overall performance. Proposed model is experimented on pedestrian video gives an improved accuracy detection rate than single detector techniques.


Keywords


Faster R-CNN, Visual Geometry Group, MOT, Computer Vision, Regression.


  1. B. Geluvaraj, P. M. Satwik, and T. A. Ashok Kumar, “The Future of Cybersecurity: Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Cyberspace,” Lecture Notes on Data Engineering and Communications Technologies, pp. 739 –747, Sep. 2018, doi: 10.1007/978-981-10-8681-6_67.
  2. G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artificial Intelligence Review, vol. 52, no. 1, pp. 77–124, Jan. 2019, doi: 10.1007/s10462-018-09679-z.
  3. J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, “Deep learning for sensor-based activity recognition: A survey,” Pattern Recognition Letters, vol. 119, pp. 3–11, Mar. 2019, doi: 10.1016/j.patrec.2018.02.010.
  4. J. E. van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Machine Learning, vol. 109, no. 2, pp. 373–440, Nov. 2019, doi: 10.1007/s10994-019-05855-6.
  5. D. Kwon, H. Kim, J. Kim, S. C. Suh, I. Kim, and K. J. Kim, “A survey of deep learning-based network anomaly detection,” Cluster Computing, vol. 22, no. S1, pp. 949–961, Sep. 2017, doi: 10.1007/s10586-017-1117-8.
  6. K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “Deep Reinforcement Learning: A Brief Survey,” IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26–38, Nov. 2017, doi: 10.1109/msp.2017.2743240.
  7. S. Krig, “Feature Learning and Deep Learning Architecture Survey,” Computer Vision Metrics, pp. 375–514, 2016, doi: 10.1007/978-3-319- 33762-3_10.
  8. W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11–26, Apr. 2017, doi: 10.1016/j.neucom.2016.12.038.
  9. Q. Zhang, L. T. Yang, Z. Chen, and P. Li, “A survey on deep learning for big data,” Information Fusion, vol. 42, pp. 146 –157, Jul. 2018, doi: 10.1016/j.inffus.2017.10.006.
  10. S. Dargan, M. Kumar, M. R. Ayyagari, and G. Kumar, “A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning,” Archives of Computational Methods in Engineering, vol. 27, no. 4, pp. 1071–1092, Jun. 2019, doi: 10.1007/s11831-019-09344-w.
  11. W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11–26, Apr. 2017, doi: 10.1016/j.neucom.2016.12.038.
  12. J. Zhai, S. Zhang, J. Chen, and Q. He, “Autoencoder and Its Various Variants,” 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 2018, doi: 10.1109/smc.2018.00080.
  13. R. Thirukovalluru, S. Dixit, R. K. Sevakula, N. K. Verma, and A. Salour, “Generating feature sets for fault diagnosis using denoising stacked auto-encoder,” 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Jun. 2016, doi: 10.1109/icphm.2016.7542865.
  14. L. Wen, L. Gao, and X. Li, “A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 1, pp. 136–144, Jan. 2019, doi: 10.1109/tsmc.2017.2754287.
  15. E. Q. Wu, G.-R. Zhou, L.-M. Zhu, C.-F. Wei, H. Ren, and R. S. F. Sheng, “Rotated Sphere Haar Wavelet and Deep Contractive Auto-Encoder Network With Fuzzy Gaussian SVM for Pilot’s Pupil Center Detection,” IEEE Transactions on Cybernetics, vol. 51, no. 1, pp. 33 2–345, Jan. 2021, doi: 10.1109/tcyb.2018.2886012.
  16. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artificial Intelligence Review, vol. 53, no. 8, pp. 5455–5516, Apr. 2020, doi: 10.1007/s10462-020-09825-6.
  17. D.-T. Hoang and H.-J. Kang, “A survey on Deep Learning based bearing fault diagnosis,” Neurocomputing, vol. 335, pp. 327–335, Mar. 2019, doi: 10.1016/j.neucom.2018.06.078.
  18. B. Shiva Prakash, K. V. Sanjeev, R. Prakash, and K. Chandrasekaran, “A Survey on Recurrent Neural Network Architectures for Sequential Learning,” Soft Computing for Problem Solving, pp. 57–66, Oct. 2018, doi: 10.1007/978-981-13-1595-4_5
  19. M. Pak and S. Kim, “A review of deep learning in image recognition,” 2017 4th International Conference on C omputer Applications and Information Processing Technology (CAIPT), Aug. 2017, doi: 10.1109/caipt.2017.8320684.
  20. N. Das, E. Hussain, and L. B. Mahanta, “Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network,” Neural Networks, vol. 128, pp. 47–60, Aug. 2020, doi: 10.1016/j.neunet.2020.05.003

Acknowledgements


Authors thank Reviewers for taking the time and effort necessary to review the manuscript.


Funding


No funding was received to assist with the preparation of this manuscript.


Ethics declarations


Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.


Availability of data and materials


No data available for above study.


Author information


Contributions

All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.


Corresponding author


Rights and permissions


Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/


Cite this article


Divya G, Manoj Kumar D S, Shri Bharathi S V, “Multiple Object Detection on Surveillance Videos for Improving Accuracy Using Enhanced Faster R-CNN”, Journal of Machine and Computing, vol.3, no.4, pp. 505-516, October 2023. doi: 10.53759/7669/jmc202303042.


Copyright


© 2023 Divya G, Manoj Kumar D S, Shri Bharathi S V. 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.