ANALYSING THE SAFETY OF THE ENVIRONMENT BY DETECTING AND COUNTING PEOPLE

Authors

  • Pendyala Himaja1 Undergraduate student, Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology.
  • Kanishk Kalkar Undergraduate student, Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology.
  • Kalyani Rajput1 Undergraduate student, Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology.
  • Karan Parate Undergraduate student, Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology.
  • Prof. Prajakta Kharwandikar Assistant Professor, Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology.

Keywords:

Person Detection, Person Count, Analysing the safety, COCO dataset

Abstract

Person detection and analysing the safety of the environment is a crucial topic in computer vision and image processing. It involves detecting people in images, videos or real-time, counting them, and assessing the safety of the environment based on the count. This technique has diverse applications in security, transportation, and healthcare. Recent advances in deep learning-based object detection algorithms, like YOLOv3 and CNN, have made person detection and counting more accurate and efficient. Safety analysis can be done by comparing the number of people in specific areas with a threshold value and taking necessary actions to enhance security. This paper reviews state-of-the-art person detection and counting techniques and discusses their applications in safety analysis. Future directions include real-time processing and integration with other sensing technologies.

References

Pooja Gupta, Varsha Sharma, Sunita Varma, “People detection and counting using YOLOv3 and SSD models”. 2. H. Zhao, Z. Li, L. Fang, T. Zhang, A Balanced “Feature Fusion SSD for Object Detection, Neural Process”. Lett. 51 (3) (2020) 2789–2806, https://doi.org/10.1007/s11063-020-10228-5. 3. A. Rastogi, B.S. Ryuh, “Teat detection algorithm: YOLO vs Haar-cascade”, J. Mech.Sci. Technol. 33 (4) (2019) 1869–1874, https://doi.org/10.1007/s12206-019-0339-5. 4. V. A. Sindagi and V. M. Patel, “CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting,” in 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017, pp. 1–6.

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Published

29-04-2023

How to Cite

Pendyala Himaja1, Kanishk Kalkar, Kalyani Rajput1, Karan Parate, & Prof. Prajakta Kharwandikar. (2023). ANALYSING THE SAFETY OF THE ENVIRONMENT BY DETECTING AND COUNTING PEOPLE. International Journal for Research Publication and Seminar, 14(3), 93–101. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/474

Issue

Vol. 14 No. 3 (2023): Vol 14 | Issue 3 | NCASIT 2023

Section

Original Research Article

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