SmartCrowd: AI-Powered Real-Time Crowd Management and Communication System
Abstract
Effective crowd control and monitoring are now crucial for maintaining public safety, resource
optimization, and operational efficiency due to the fast urbanization and population
growth. Conventional techniques for counting individuals and detecting crowds rely on
manual observation, which is laborious, prone to errors, and unsuitable for real-time applications
in dynamic settings. The goal of this project is to use cutting-edge computer vision
and deep learning techniques to create an automatic real-time crowd recognition and people
counting system.
The system makes use of the cutting-edge object detection model YOLOv3 (You Only
Look Once), which is renowned for its accuracy and speed. Even in congested and complicated
surroundings, YOLOv3 reliably detects people in real time by processing the input
video feed. The SORT (Simple Online and Realtime Tracking) algorithm, which employs
the Hungarian algorithm for object association across video frames and Kalman Filters for
motion prediction, is incorporated to preserve continuity and guarantee precise person
counting. When combined, YOLOv3 and SORT provide a scalable and resilient system
that can manage dense crowds while resolving issues like occlusions, overlapping objects,
and changing illumination.
Live video streams from webcams, IP cameras, and pre-recorded video are processed by the
system. In addition to tracking people's movements and dynamically counting the number of
individuals in the screen, it also displays visual cues like tracking IDs and bounding boxes.
In order to keep an eye on crowd flow, the system can also recognize line-crossing events,
such entry or exit. A user-friendly interface is used to display the results, and HTTP APIs can
be used to integrate them with other systems.
By automated crowd identification and person counting, this technology helps sectors like
public safety, event planning, retail analytics, and transit hubs. It offers accurate, real-time
insights while lowering human labor and mistakes. It improves operational efficiency and
safety in crowded areas with its great precision and scalability.