Human-Object Interaction Detection with RF-DETR

Human-object interaction detection identifies people, objects, and their apparent relationships, such as a worker operating a forklift or pushing a cart, extending standard object detection by determining who is doing what with which object. The guide presents two Roboflow Workflows that avoid manually labeled interaction data by using RF-DETR to localize warehouse workers and equipment and Google Gemini vision-language models to infer actions from annotated images. The first workflow uses a five-class warehouse detector and Gemini 2.5 Pro to generate cautious scene-level interaction summaries, while the second uses a forklift-person detector, person-centered crops, and Gemini 2.5 Flash-Lite to classify each detected person as safe or unsafe and produce a structured JSON report. The article reports validation metrics for the scene detector, describes workflow components for visualization, cropping, event logging, and deployment, and emphasizes that VLM-based judgments are less deterministic than dedicated trained interaction models. It recommends testing under real warehouse conditions, reviewing uncertain or flagged results, and using collected events to refine prompts, monitor trends, and develop more focused safety rules over time.

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