Computer Vision Techniques for Real-Time Crowd Monitoring and Safety Management
Article Sidebar
Main Article Content
Abstract
Crowd management is critical for ensuring safety during large public events. This paper presents a computer vision-based system for real-time crowd monitoring using surveillance cameras. Deep learning models are employed to detect crowd density, movement patterns, and potential anomalies. The proposed system enables early detection of hazardous situations, improving response times for authorities. Experimental results show reliable performance in diverse environments, making it suitable for smart city applications.
Article Details
References
Asopa, P., Purohit, P., Nadikattu, R. R., & Whig, P. (2021). Reducing carbon footprint for sustainable development of smart cities using IoT. In Proceedings of the Third International Conference on Intelligent Communication Technologies.
Whig, P., Velu, A., & Nadikattu, R. R. (2022). The economic impact of AI-enabled blockchain in 6G-based industry. In AI and blockchain technology in 6G wireless network (pp. 205–224).
Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain platform to resolve security issues in IoT and smart networks. In AI-enabled agile internet of things for sustainable FinTech ecosystems (pp. 46–65).
Vaddadi, S. A., Vallabhaneni, R., & Whig, P. (2023). Utilizing AI and machine learning in cybersecurity for sustainable development through enhanced threat detection and mitigation. International Journal of Sustainable Development Through AI, ML and IoT, 2(2).
Vemulapalli, G., Yalamati, S., Palakurti, N. R., Alam, N., Samayamantri, S., & Whig, P. (2024). Predicting obesity trends using machine learning from big data analytics approach. In Proceedings of the Asia Pacific Conference on Innovation in Technology (APCIT) (pp. 1–5).
Whig, P., Kasula, B. Y., Yathiraju, N., Jain, A., & Sharma, S. (2024). Revolutionizing gender-specific healthcare: Harnessing deep learning for transformative solutions. In Transforming gender-based healthcare with AI and machine learning (pp. 14–26).