• Snehitha Mugidi
• V. Neelima devi

This project aims to leverage the capabilities of computer vision to enhance human safety in crowded areas. By using Python and computer vision techniques, the system can detect people and objects in a crowd and analyze their movements and interactions. This technology can be used to prevent accidents and save lives by providing real-time visual feedback to security personnel or emergency responders. The project includes the development of a user-friendly interface that allows security personnel to access real-time footage and analyze the data generated by the system. This interface includes a dashboard that provides a summary of the crowd conditions and alerts and allows users to adjust the system parameters to optimize performance. This project demonstrates the potential of computer vision to enhance human safety in crowded areas, by providing real-time visual feedback to detect and prevent accidents. By leveraging the capabilities of Python and computer vision techniques, this system can provide a valuable tool for security personnel and emergency responders to prevent accidents due to over crowdedness and save lives.

Keywords: opencv, time, video, computer-vision, deep-learning, tensorflow, numpy, detection, python3 tkinter, matplotlib, counting, cv2, human-detection, detection-model, faster-rcnn-inception-v2

"Leveraging Human Safety through Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.j759-j770, July-2023, Available :http://www.jetir.org/papers/JETIR2306990.pdf

"Leveraging Human Safety through Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppj759-j770, July-2023, Available at : http://www.jetir.org/papers/JETIR2306990.pdf