AI-Powered Multi-Camera Tracking

Historically, multi-camera tracking has been difficult because it involves matching objects across different camera views, requiring precise camera calibration and synchronization. Challenges include varying camera angles, lighting, and video quality. Handling non-overlapping views and fusing information for accurate and consistent IDs across cameras adds even more complexity.

The NVIDIA DeepStream SDK multi-camera tracking (MCT) plug-in provides consistent 3D object tracking across multiple overlapping, calibrated cameras. And setup can be completed in less than five minutes, including global IDs, real-time fusion, and scalable deployment for retail, warehouses, and beyond.

In this demo, a digital twin of a warehouse–built using NVIDIA Omniverse™ libraries–operates as a simulation environment for dozens of digital workers and multiple autonomous robots. Integrated into the NVIDIA Mega virtual factory workflow, the multi-camera tracking workflow enabled the creation of a centralized map of the entire warehouse with 100 simulated cameras, providing real-time awareness of the physical space.

NVIDIA provides the software tools for the entire development pipeline—from data generation to model training to application development—to help developers build complex vision AI applications for large spaces, fast.

• NVIDIA Omniverse libraries for creating 3D digital twins of real-world environments.
  Use NVIDIA Omniverse—a collection of libraries and microservices—to construct 3D digital twins of real-world spaces, position virtual cameras to capture diverse synthetic data, generate ground-truth annotations for training perception models, and validate end-to-end applications prior to real-world deployment.

• NVIDIA Isaac Sim™ for synthetic data generation to optimize training..
The Action and Event Generation application kit within Isaac Sim simplifies agent simulation—including people and autonomous moving robots—to streamline the generation of synthetic data from scenes.

• NVIDIA TAO toolkit for streamlined model development with real and synthetic data.
NVIDIA TAO simplifies model training and optimization for tasks such as people detection and re-identification. Developers can fine-tune pretrained models with synthetic and real data and optimize for better inference performance through quantization and pruning.

• DeepStream for Multi-Camera Tracking (MCT) across a range of cameras.
MCT extends DeepStream NvTracker to enable distributed, real-time 3D tracking across a network of cameras. DeepStream automatically assigns unique IDs for new objects, preserving identity through occlusions and handovers. MCT works seamlessly with both 2D and 3D detectors, supporting a wide range of use cases.

• Cloud-native design for scalable deployment from edge to cloud.
Containerize your applications easily with Docker, Kubernetes, and GPU Operators to deploy cloud-native solutions on NVIDIA Jetson™, x86, and dGPU.
 

Jumpstart development of multi-camera vision AI applications with the NVIDIA accelerated end-to-end workflow—from Omniverse libraries for synthetic data generation to TAO for streamlined model development to Metropolis for modular, cloud-native application building blocks.