ARCHIVES

[1] Nalwa, N., Bhadola, S., Bhatia, K. and Sharma, R., 2022. A Detailed Study on People Tracking Methodologies in Different
Scenarios. International Journal of Innovative Research in Computer and Communication Engineering, 10(6), pp.5907-5913.
[2] Nalwa, N., Bhadola, S., Bhatia, K. and Sharma, R.,2022. Development of Vision Based People Counting Framework using Webcam.
International Journal of Multidisciplinary Research in Science, Engineering, and Technology, Volume 5, Issue 7, July 2022, pp.
1665-1671.
[3] Fareed, S., Bhatia, K., Bhadola, S. and Sharma, R., 2022. An Overview of Object Tracing Techniques in Videos and Images. Central
Asian Journal of Theoretical and Applied Science, 3(7), pp.146-156.
[4] Attri, M.K., Bhatia, K., Bhadola, S. and Sharma, R., 2022.An Image Sharpening and Smoothing Approaches Analysis. International
Journal of Innovative Research in Computer and Communication Engineering, 10(6), pp. 5573-5580.
[5] Jain, P., Arora, M. and Sharma, R., 2024. Exploring Image Processing Based Object Detection & Tracking Techniques in Videos:
A Concise Overview. International Journal of Innovative Research in Engineering, 5(3), pp. 180-185.[6] Jain, S., Arora, M. and Sharma, R., 2024.Reconnoitering Image Segmentation Methods: Techniques, Challenges, and Trends.
International Journal of Scientific Research in Engineering & Technology, 4(3), pp: 72-77.
[7] Singh, A., Arora , M., & Sharma, R. 2025. Image Processing and Machine Learning Approaches for Leaf Disease Identification: A
Survey. Excellencia: International Multi-Disciplinary Journal of Education (2994-9521), 3(6), 51-61. https://doi.org/10.5281/
[8] Singh, A., Arora , M., & Sharma , R. 2025. Automated Leaf Disease Identification via Image Processing and Multi-Class Support
Vector Machine. International Journal of Innovative research of Science, Engineering and Technology, 14(6).
[9] Y.-L. Hou and G. K. Pang, “People counting and human detection in a challenging situation,” IEEE transactions on systems, man,
and cybernetics-part a: systems and humans, vol. 41, no. 1, pp. 24–33, 2010.
[10] N. A. Othman, M. U. Salur, M. Karakose, and I. Aydin, “An embedded real-time object detection and measurement of its size,” in
2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018, pp. 1–4.
[11] Ali, M.A., Jaafar, A. and Sadiq, A.T., 2022. Detection and count of human bodies in a crowd scene based on enhancement features
by using the YOLO v5 algorithm. Iraqi Journal of Computers, Communications, Control and Systems Engineering, 22(2), p.11.
[12] Ren, P., Wang, L., Fang, W., Song, S. and Djahel, S., 2020. A novel squeeze YOLO-based real-time people counting approach.
International Journal of Bio-Inspired Computation, 16(2), pp.94-101.
[13] W. Liu, M. Salzmann, and P. Fua, “Counting people by estimating people flows,” IEEE transactions on pattern analysis and machine
intelligence, vol. 44, no. 11, pp. 8151–8166, 2021
[14] M. B. Shami, S. Maqbool, H. Sajid, Y. Ayaz, and S.-C. S. Cheung, “People counting in dense crowd images using sparse head
detections,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 9, pp. 2627–2636, 2018.
[15] Chen, J., 2024, July. Crowd Counting and People Density Detection: An Overview. In Proceedings of the 2024 3rd International
Conference on Engineering Management and Information Science (EMIS 2024) (p. 434). Springer Nature.
[16] J.-H. Choi, J.-E. Kim, and K.-T. Kim, “People counting using ir-uwb radar sensor in a wide area,” IEEE Internet of Things Journal,
vol. 8, no. 7, pp. 5806–5821, 2020.
[17] J.-H. Choi, J.-E. Kim, and K.-T. Kim, “Deep learning approach for radar-based people counting,” IEEE Internet of Things Journal,
vol. 9, no. 10, pp. 7715–7730, 2021.
[18] Z. Liu, R. Yuan, Y. Yuan, Y. Yang, and X. Guan, “A sensor-free crowd counting framework for indoor environments based on
channel state information,” IEEE Sensors Journal, vol. 22, no. 6, pp. 6062–6071, 2022.
[19] A. M. Hayajneh, S. Aldalahmeh, S. A. R. Zaidi, D. McLernon, H. Obeidollah, and R. Alsakarnah, “Channel state information based
device free wireless sensing for iot devices employing tinyml,” in 2022 4th IEEE Middle East and North Africa COMMunications
Conference (MENACOMM). IEEE, 2022, pp. 215–222.
[20] M. De Sanctis, S. Di Domenico, D. Fioravanti, E. B. Abellán, T. Rossi, and E. Cianca, “Rf-based device-free counting of people
waiting in line: A modular approach,” IEEE Transactions on Vehicular Technology, vol. 71, no. 10, pp. 10471–10484, 2022.
[21] S. T. Kouyoumdjieva, P. Danielis, and G. Karlsson, “Survey of non-image-based approaches for counting people,” IEEE
Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1305–1336, 2019.