ML Computer Vision

Challenge

Modern public spaces like retail centers, urban environments, and large events face growing pressure to understand how people move through and interact with spaces. Traditional CCTV setups provide raw footage but lack the intelligence to extract actionable insights. Key challenges included:

• • Accurate real-time person detection in crowded or dynamic environments
• • Seamless tracking across multiple camera angles and locations
• • Need for data-driven insights for space optimization and crowd management

Solution

Next Halo developed a scalable, real-time machine learning system for multi-camera person detection and tracking. Our approach combined:

• • Advanced computer vision models to enable precise detection, even in complex, crowded scenes
• • Multi-camera integration for continuous tracking of individuals across overlapping views
• • Generation of interactive heatmaps to identify high-traffic zones, dwell areas, and bottlenecks
• • A visualization platform that translates raw camera data into operational insights

Result

The delivered platform empowers stakeholders across industries to make smarter, faster decisions:

• • Unified real-time view of monitored spaces from multiple camera inputs
• • Clear, interactive visualizations showing movement patterns and crowd density
• • Improved spatial planning, resource allocation, and crowd control strategies
• • Applicable to retail analytics, urban design, event logistics, and more

Tech Stack

Driven by a secure, production-grade architecture that’s both scalable and maintainable—perfect for rapid, iterative delivery:

LangChain

LangGraph

FastAPI

vLLM

React.js

NestJS

Keycloak

Python

Azure Cloud