CrowdFlow Analytics System
An AI-driven CrowdFlow Analytics System for real-time crowd detection, density analysis, and safety alerts in public environments. Developed using YOLOv5, OpenCV, Flask, and Raspberry Pi with support for people counting and live heatmap visualization.
Real-Time Computer Vision Pipeline
Overview
CrowdFlow Analytics is an edge-deployed computer vision platform engineered to monitor pedestrian density, estimate crowd counts, and trigger safety alerts in densely populated venues.
Problem
High-footfall environments like transit terminals, religious gatherings, and stadiums are vulnerable to sudden stampedes and bottlenecks without automated spatial density monitoring.
Solution
Developed an optimized deep-learning vision pipeline using YOLOv5 for rapid object localization, tracking pedestrian bounding boxes and generating live heatmaps on low-power edge hardware.
Technology Stack
Engineered with YOLOv5 deep learning models, OpenCV computer vision processing, Flask backend services, and lightweight deployment targeting Raspberry Pi microcomputers.
- YOLOv5 model fine-tuned for high-angle overhead crowd detection
- OpenCV video frame ingestion, normalization, and heatmap generation
- Flask REST API streaming metrics and alert webhooks
- Raspberry Pi edge inference with lightweight quantization
Challenges
Maintaining robust real-time detection frame rates (15+ FPS) under tight computational and memory constraints on edge devices.
Future Scope
Integrating bi-directional optical flow for panic vector detection and multi-camera spatial handoff algorithms.