Abstract
Monitoring crowds in public areas is essential for maintaining safety and proper management. The traditional way of crowd monitoring is usually ineffective and prone to errors. In this research paper, we propose an approach that involves designing and implementing a Real-Time Crowd Monitoring System through the use of sensors and computer vision to monitor crowds automatically. The system analyzes crowd levels in real time and generates alerts when density exceeds a predefined limit. The proposed system improves surveillance efficiency, reduces human effort, and enhances public safety in crowded environments. Experimental evaluation demonstrates efficient stream processing with low latency and high alert accuracy.
References
- Girshick, Ross, Jeff Donahue, Trevor Darrell, and Jitendra Malik. "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, 580-587.
- Dalal, Navneet, and Bill Triggs. "Histograms of Oriented Gradients for Human Detection." In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol. 1, 886-893.
- Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You Only Look Once: Unified, Real-Time Object Detection." In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 779-788.
- OpenCV Developers, “OpenCV: Open Source Computer Vision Library,” 2023. [Online]. Available: https://opencv.org
- Chan, Antoni B., Zhang-Sheng John Liang, and Nuno Vasconcelos. "Privacy Preserving Crowd Monitoring: Counting People Without People Models or Tracking." In 2008 IEEE conference on computer vision and pattern recognition, 1-7.
- Ali, Saad, and Mubarak Shah. "A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis." In 2007 IEEE conference on computer vision and pattern recognition, 1-6.
- Loy, Chen Change, Ke Chen, Shaogang Gong, and Tao Xiang. "Crowd Counting and Profiling: Methodology and Evaluation." In Modeling, simulation and visual analysis of crowds: A multidisciplinary perspective, New York, NY: Springer New York, 2013, 347-382.
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks." Communications of the ACM 60, no. 6 (2017): 84-90.
- Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. "Ssd: Single Shot Multibox Detector." In European conference on computer vision, Cham: Springer International Publishing, 2016, 21-37.
- He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep Residual Learning for Image Recognition." In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 770-778.
- Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully Convolutional Networks for Semantic Segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, 3431-3440.
- Lowe, David G. "Distinctive Image Features from Scale-Invariant Keypoints." International journal of computer vision 60, no. 2 (2004): 91-110.
- Viola, Paul, and Michael Jones. "Rapid Object Detection Using a Boosted Cascade of Simple Features." In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol. 1, I-I.
- Bouwmans, Thierry. "Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview." Computer science review 11 (2014): 31-66.
- Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." IEEE transactions on pattern analysis and machine intelligence 39, no. 6 (2016): 1137-1149.
- Everingham, Mark, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. "The Pascal Visual Object Classes (Voc) Challenge." International journal of computer vision 88, no. 2 (2010): 303-338.
- Bradski, Gary. "The Opencv Library." Dr. Dobb's Journal: Software Tools for the Professional Programmer 25, no. 11 (2000): 120-123.
- Szeliski, Richard. Computer Vision: Algorithms And Applications. Springer Nature, 2022.

Journal of Ubiquitous Computing and Communication Technologies