Modified Neural Network-based Object Classification in Video Surveillance System

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.890
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Volume 13, Special Issue 3, October 2022

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Rakhi Joshi Bhardwaj
Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana, Working as Assistant Professor at Vishwakarma Institute of Technology, Pune
D.S. Rao
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation,Deemed to be University, Hyderabad, Telangana.

Abstract

Visual surveillance emerged as an active automated research area of Computer Vision from the traditional mathematical approach to neural networks. A novel modified neural network technique for object detection and classification for input images and video feed from many cameras overlapping target areas is presented in this research.Modified Neural Network methodology represents layered architecture as the input, preprocessing and Operation layer, to simplify the processing needed to prepare for training neural networks. This strategy aids in delegating the tasks to layers with predefined tasks thus simplifying training, reducing computational requirements, and delivering performance. Two modules of the Neural Network will process the input. The first module is a modified Neural Network and will differ from traditional Neural Network in respect of connectivity between Neurons and their operations. This will still be Neural Network for data shared and threshold followed for marking differences – Markers, between the two inputs and simplified training. The second Module will be a traditional Neural Network for detection and classification that will track the detected objects. This paper proposed a system that provides the combined image as an output from multiple cameras feed using an untraditional Mathematical and Algorithmic Approach.

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Creative Commons License

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

How to Cite
Bhardwaj, R. J. ., & Rao, D. (2022). Modified Neural Network-based Object Classification in Video Surveillance System. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.890
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References

  1. An efficient way of text-based emotion analysis from social media using lra-dnn. Neuro- science Informatics 2, 3, 100048. DOI: https://doi.org/10.1016/j.neuri.2022.100048
  2. Alshammari, A. and Rawat, D. B. 2019. Intelligent multi-camera video surveillance system for smart city applications. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). 0317–0323. DOI: https://doi.org/10.1109/CCWC.2019.8666579
  3. Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. 2010. The pascal visual object classes (voc) challenge. International journal of computer vision 88, 2, 303–338. DOI: https://doi.org/10.1007/s11263-009-0275-4
  4. Girshick, R. and Malik, J. 2013. Training deformable part models with decorrelated features. In Proceedings of the IEEE International Conference on Computer Vision. 3016–3023. DOI: https://doi.org/10.1109/ICCV.2013.375
  5. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. 2016. Deep learning for visual understanding: A review. Neurocomputing 187, 27–48. Recent Developments on Deep Big Vision. DOI: https://doi.org/10.1016/j.neucom.2015.09.116
  6. He, K., Zhang, X., Ren, S., and Sun, J. 2015. Spatial pyramid pooling in deep convolu- tional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 37, 9, 1904–1916. DOI: https://doi.org/10.1109/TPAMI.2015.2389824
  7. Khan, A., Sohail, A., Zahoora, U., and Qureshi, A. S. 2020. A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review 53, 8, 5455–5516. DOI: https://doi.org/10.1007/s10462-020-09825-6
  8. Mark, E., SM, E., Luc, V. G., KI, W. C., John, W., and Andrew, Z. 2015. The pas- cal visual object classes challenge: A retrospective. International journal of computer vision 111, 1, 98–136. DOI: https://doi.org/10.1007/s11263-014-0733-5
  9. Gupta, A. and Prabhat, P., 2022. Towards a resource efficient and privacy-preserving framework for campus-wide video analytics-based applications. Complex & Intelligent Systems, pp.1-16. DOI: https://doi.org/10.1007/s40747-022-00783-w
  10. Nam, H. and Han, B. 2016. Learning multi-domain convolutional neural networks for visual tracking. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4293–4302. DOI: https://doi.org/10.1109/CVPR.2016.465
  11. Raj, L. M. I. L. J. D. P. S. A. K. D. S. L. B. D. A. N. D. K. B. and Gulati, D. K. 2021. Methods to identify facial detection in deep learning through the use of real-time training datasets management. EFFLATOUNIA - Multidisciplinary Journal 5, 2, 1298 –1311.
  12. Ross, G., Jeff, D., Trevor, D., and Jitendra, M. 2015. Region-based convolutional net- works for accurate object detection and segmentation. IEEE transactions on pattern anal- ysis and machine intelligence 38, 1, 142–158. DOI: https://doi.org/10.1109/TPAMI.2015.2437384