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Machine Learning Based Webcasting Analytics for Indian Elections - Reflections on Deployment

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

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Abstract

India is the largest democracy in the world. Elections play an irreplaceable role in reverberating the voice of its citizens in the governing institutions of this country. To safeguard the power vested in the people, it is essential that the voting process is safe, fair and transparent. This can be very well ensured by effective surveillance of polling activities and analysis of the real-time data that can be gathered from the polling stations. The scattered and widespread locations of the polling stations also requires proper synchronization and organization of the data collection and analysis processes. This paper presents a machine learning-based web analytics system specifically designed to monitor polling stations during the Goa State Assembly elections in 2022. The system accepts as input the CCTV video feeds generated by Internet Protocol (IP) cameras from several polling centres and processes it optimally to produce and transmit analytical information in real-time to the command and control centre. We also highlight the practical challenges faced in deploying this system and their resolution. The successful implementation of this system for the Goa Assembly Elections 2022, first of its kind in India, demonstrates the effectiveness and reliability of this approach.

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References

  1. Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Computational Intelligence and Neuroscience 2018 (2018)

    Google Scholar 

  2. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788, IEEE (2016)

    Google Scholar 

  3. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE (2014)

    Google Scholar 

  4. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  5. Wojke, N., Bewley, A., Paulus, D.: Simple online and real-time tracking with a deep association metric. In: IEEE International Conference on Image Processing (ICIP), pp. 3645–3649. IEEE (2017)

    Google Scholar 

  6. Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: FairMOT: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vision 129(11), 3069–3087 (2021). https://doi.org/10.1007/s11263-021-01513-4

    Article  Google Scholar 

  7. Chu, P., Wang, J., You, Q., Ling, H., Liu, Z.: Transmot: spatial-temporal graph transformer for multiple object tracking. arXiv preprint arXiv:2104.00194 (2021)

  8. Nguyen, D.T., Wanqing, L., Ogunbona, P.O.: Human detection from images and videos: a survey. Pattern Recogn. 51, 148–175 (2016)

    Article  Google Scholar 

  9. Dahlan, I.A., Putra, M.B.G., Supangkat, S.H., Hidayat, F., Lubis, F.F., Hamami, F.: Real-time passenger social distance monitoring with video analytics using deep learning in railway station. Indonesian J. Electr. Eng. Comput. Sci. 26(2), 773–784 (2022)

    Google Scholar 

  10. Ahmed, I., Jeon, G.: A real-time person tracking system based on SiamMask network for intelligent video surveillance. J. Real-Time Image Proc. 18(5), 1803–1814 (2021). https://doi.org/10.1007/s11554-021-01144-5

    Article  Google Scholar 

  11. Zhou, Y.: Deep learning based people detection, tracking and re-identification in intelligent video surveillance system. In: International Conference on Computing and Data Science (CDS), pp. 443–447. IEEE (2020)

    Google Scholar 

  12. Portmann, J., Lynen, S., Chli, M., Siegwart, R.: People detection and tracking from aerial thermal views. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1794–1800. IEEE (2014)

    Google Scholar 

  13. Muñoz-Salinas, R., Aguirre, E., García-Silvente, M.: People detection and tracking using stereo vision and color. Image Vis. Comput. 25(6), 995–1007 (2007)

    Article  MATH  Google Scholar 

  14. Schulzrinne, H., Rao, A., Lanphier, R.: RFC2326: Real time streaming protocol (RTSP). RFC Editor, USA (1998)

    Google Scholar 

  15. Schulzrinne, H., Casner, S., Frederick, R., Jacobson, V.: RFC3550: RTP: a transport protocol for real-time applications. RFC Editor, USA (2003)

    Google Scholar 

  16. Berners-Lee, T., Fielding, R., Frystyk, H.: RFC1945: Hypertext Transfer Protocol - HTTP/1.0. RFC Editor, USA (1996)

    Google Scholar 

  17. Bochkovskiy, A., Chien-Yao, W., Hong-Yuan Mark, L.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  18. YOLOv5 Github. https://github.com/ultralytics/yolov5. Accessed 13 July 2022

  19. Parico, A.I.B., Ahmad, T.: Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT. Sensors 21(14) (2021). https://doi.org/10.3390/s21144803

  20. Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 749–765. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_45

    Chapter  Google Scholar 

  21. Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Quarterly 2, 83–97 (1955). https://doi.org/10.1002/nav.3800020109

    Article  MathSciNet  MATH  Google Scholar 

  22. Welch, G., Bishop, G.: An introduction to the Kalman filter. SIGGRAPH 2001, Course 8, Los Angeles (2001)

    Google Scholar 

  23. Kirk, D.: NVIDIA CUDA software and GPU parallel computing architecture. In: Proceedings of the 6th International Symposium on Memory Management, pp. 103–104, ACM (1997)

    Google Scholar 

  24. Shee Thoo, S.J.H.: Visual recognition using artificial intelligence (person detection and tracking using artificial intelligence). Final Year Project (FYP), Nanyang Technological University, Singapore (2022)

    Google Scholar 

  25. Lei, Y.A.N.G., Shao-yun, W.A.N.G., Li-ran, L.I.U., Yong-fu, G.O.N.G.: A pedestrian detection method in intelligent video monitoring system. Comput. Modernization 11, 69 (2019)

    Google Scholar 

  26. Liciotti, D., Paolanti, M., Frontoni, E., Zingaretti, P.: People detection and tracking from an RGB-D camera in top-view configuration: review of challenges and applications. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 207–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70742-6_20

    Chapter  Google Scholar 

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Acknowledgement

The authors acknowledge the support provided by the Election Commission of India (ECI) and the officers from the Office of Chief Electoral Officer (CEO), Goa, especially Shri Kunal, IAS, CEO Goa and Praveen Volvotkar, Nodal Officer (IT) and Joint Director, Department of Information Technology, Goa in piloting the proposed system.

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Authors and Affiliations

  1. Indian Institute of Technology Goa, Ponda, 403401, India

    Aditi Saxena & Sharad Sinha

Authors
  1. Aditi Saxena
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  2. Sharad Sinha
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Corresponding author

Correspondence to Sharad Sinha .

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Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Saxena, A., Sinha, S. (2023). Machine Learning Based Webcasting Analytics for Indian Elections - Reflections on Deployment. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-31417-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

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