Crowd Monitoring For Shops And Malls

DOI

10.2991/978-94-6463-858-5_222How to use a DOI?

Keywords

Crowd Monitoring; Computer Vision; MobileNet-SSD; Deep Learning; Object Detection; Real-Time Analytics; Centroid Tracking; Heatmap Generation; OpenCV

Abstract

Efficient crowd management is crucial for ensuring a seamless user experience, optimizing operations, and minimizing safety risks in public spaces such as malls and transit areas. With the increasing number of pedestrians, issues like overcrowding, stampede-like situations, long queues, inefficient space utilization, and poor resource allocation have become major concerns. These challenges not only deteriorate the user experience but also pose significant safety hazards and complicate real-time regulatory efforts. Traditional crowd monitoring methods, such as manual supervision and threshold-based counters, often neglect critical factors like the spatial area, peak times, and hotspot zones, making them unreliable and unscalable for dynamic environments. To address these limitations, this study proposes an AI-enhanced Crowd Monitoring System that integrates MobileNet-SSD for deep learning-based object detection with OpenCV for real-time image processing. The system further utilizes centroid tracking to ensure accurate and continuous identification of individuals across video frames. This approach enables predictive analytics and precise crowd density estimation in real time. The results demonstrate that the system can effectively detect and track people with high accuracy and responsiveness, making it suitable for deployment in live environments. In conclusion, the proposed system offers a scalable, reliable, and intelligent solution for crowd monitoring, capable of significantly improving safety, operational efficiency, and user satisfaction in densely populated public areas.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sangita Lade
AU  - Sudhakar Shinde
AU  - Samarth Swami
AU  - Sneha Katole
AU  - Prabhakar Uparkar
PY  - 2025
DA  - 2025/11/04
TI  - Crowd Monitoring For Shops And Malls
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2671
EP  - 2679
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_222
DO  - 10.2991/978-94-6463-858-5_222
ID  - Lade2025
ER  -