CASE STUDY
Multi-Target Multi-Camera Tracking
TechnoLynx built a cost-efficient, AI-powered tracking system using existing CCTV infrastructure, enabling real-time object tracking across non-overlapping cameras with consistent local and global IDs.
The Challenge
A multinational startup wanted a cost-efficient tracking system built on their existing CCTV-style camera infrastructure. The solution needed to track multiple targets (objects and people) within a monitored area, both within a single camera view and across multiple cameras. The challenge increased significantly when camera views didn’t overlap: the system had to keep tracking when a target disappeared from one camera and only reappeared later in another.
Work with existing CCTV infrastructure.
The system had to be accurate and real-world ready without relying on adding more cameras to “buy” accuracy.
Handle non-overlapping camera views.
Targets can leave one camera and remain unseen before entering another, creating gaps in continuous tracking.
Keep identity consistent across cameras.
Track many targets at once while preventing misassociation, especially during transitions between camera views.
Support low-cost edge and scalable deployments.
The client needed a version for edge devices (e.g., Raspberry Pi) and a more powerful option for larger-scale operation.
Project Timeline
From single-camera tracking to reliable multi-camera identity across non-overlapping views
Problem Framing
Defined the goal: track multiple targets within and across CCTV camera feeds in a cost-effective way.
Implemented real-time target detection and single-camera tracking, learning movement patterns to improve tracking.
Detection + Single-Camera Tracking
Non-Overlap Prediction
Built a probabilistic model to predict trajectories and estimate where targets are likely to appear next when cameras don’t overlap.
Introduced local IDs per camera and a global ID across the system, tuning association logic to reduce mis-matches with many simultaneous targets.
Multi-Camera Identity
Edge + HPC Delivery & Demos
Delivered an edge version for low-cost hardware (e.g., Raspberry Pi) and an HPC-backed version for larger deployments, supporting real-world demonstrations.
The Solution
We designed an AI-powered multi-camera tracking system that works within existing CCTV infrastructure and remains cost-efficient. It detects and tracks multiple targets in real time, assigns local and global identities, and uses probabilistic prediction to bridge gaps when camera views don’t overlap.
Probabilistic Prediction
A higher-level probabilistic model predicts paths and trajectories to estimate where a target will appear next, maintaining continuity across non-overlapping views.
Detection + Tracking
Integrated object detection for real-time identification, then tracked targets through each camera’s view using learned movement patterns.
Edge + HPC Versions
Delivered both an edge deployment for low-cost devices (e.g., Raspberry Pi) and a more powerful HPC-backed version for higher resolution and larger camera fleets.
Technical Specifications
Frameworks
PyTorch, TensorRT, OpenCV, NumPy + custom code
Identity Model
Local ID per camera + global ID across the full system
Non-Overlap Handling
Probabilistic trajectory prediction to bridge gaps between cameras
Edge
Designed for low-cost hardware (e.g., Raspberry Pi)
HPC
Higher-resolution, larger-scale tracking for many-camera environments
The Outcome
The final tracking system met all of the client’s key objectives. The multi-camera tracking system accurately identified and tracked objects and people as they moved within and across the cameras’ fields of view. Even in scenarios where the cameras didn’t overlap, the probabilistic model maintained an accurate prediction of the target’s movements. The system was cost-efficient, as it didn’t require the client to install additional cameras, and it could be deployed on both edge devices like Raspberry Pi and high-performance environments. The successful real-world demonstrations helped the client showcase their technology to potential partners and customers.
Key Achievements
Multi-target tracking within and across multiple cameras in real time
Probabilistic prediction to maintain tracking across non-overlapping camera views
Local + global ID system to preserve identity across the camera network
Delivered both edge (low-cost) and HPC-backed versions for different operational scales
Supported real-world demonstrations so the client could prove the system’s functionality to potential customers, impressing stakeholders.
Building a Tracking System on Existing Cameras?
Let’s discuss how multi-camera tracking, predictive modelling, and practical CV pipelines can help you scale monitoring without expensive infrastructure changes.
