NVIDIA has released DeepStream 9.1, a major update to its vision AI development framework that introduces automated multi-camera 3D tracking and calibration capabilities. The release addresses persistent limitations in video analytics, where single-camera systems frequently lose track of objects exiting the frame and conventional 3D tracking relies on labor-intensive manual calibration. The update centers on two integrated modules: Multi-View 3D Tracking and AutoMagicCalib. Multi-View 3D Tracking projects independent detections from a camera network into a shared world-coordinate system, fusing inputs to preserve a consistent object identifier across all viewpoints. This distributed architecture uses advanced multi-view association algorithms to calculate real-time positions without centralized processing bottlenecks. AutoMagicCalib eliminates the manual configuration traditionally required for such setups by automatically deriving intrinsic parameters such as focal length and lens distortion, alongside extrinsic parameters like spatial orientation. The calibration pipeline extracts object trajectories from synchronized video streams, performs single-view rectification, matches tracklets across cameras, and applies global bundle adjustment to minimize reprojection error. The system also supports optional integration with the Visual Geometry Grounded Transformer model to improve robustness when object movement is limited. DeepStream 9.1 introduces modular agentic skills tailored for AI coding assistants, fundamentally changing how vision pipelines are constructed. Developers can now describe application requirements using natural language prompts, with the framework automatically handling environment provisioning, dependency resolution, and deployment sequencing. The workflow accommodates immediate testing using pre-calibrated reference datasets and transitions smoothly to custom deployments requiring AutoMagicCalib processing. Successful deployments generate synchronized multi-camera feeds, real-time bird's-eye view trajectory visualizations, and structured tracking outputs compatible with standard edge messaging protocols. Source code, reference applications, sample datasets, and configuration templates are now available through the official NVIDIA DeepStream GitHub repository. By automating calibration and enabling agent-driven pipeline generation, DeepStream 9.1 significantly accelerates development cycles while improving tracking stability in expansive environments. The framework is optimized for edge deployment and targets enterprise use cases including warehouse safety monitoring, retail analytics, and intelligent building infrastructure. Comprehensive documentation and community support are accessible via the NVIDIA DeepStream Developer Forum.