Roboflow Video Inference with Custom Annotators

Real-time video inference is essential for applications such as autonomous vehicles and logistics, but setting up an effective pipeline can be complex and time-consuming. The process involves using a trained computer vision model, inference code, and visualization tools for annotating video frames. This article outlines a streamlined approach to scalable video inference using Roboflow Universe, which provides customizable pre-trained models that can be accessed with minimal Python code. By employing a Roboflow Logistics Object Detection Model, users can add custom annotations like boxes and labels using the Supervision library. A practical application of this technology is in logistics monitoring, where video feeds in warehouses can be used to track inventory and assets automatically, enhancing the accuracy and efficiency of inventory management. The article provides a step-by-step guide on loading a model from Roboflow Universe, running video inference, applying annotations, and extracting annotated videos, demonstrating the capability of Roboflow and Supervision to create robust, production-ready solutions.

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