As a global leader in accelerated computing advanced its physical AI systems, the company needed high-quality visual datasets to train models capable of detecting warehouse safety risks. The challenge wasn’t simply collecting video footage, it was producing structured datasets that could reliably capture real operational scenarios, from unsafe forklift operations to pedestrian proximity risks.
Working together with TP, the client built a human-in-the-loop data production framework designed to generate high-fidelity training data. Controlled warehouse simulations recreated complex safety scenarios, while synchronized multi-camera capture and frame-level annotation ensured precision and consistency across datasets. In just three weeks, the program delivered large-scale training data ready for GPU-powered AI models. The result: faster model development and more reliable detection of warehouse hazards in real time.
Key benefits included:
Delivered 100K staged warehouse video assets to support large-scale physical AI model training.
Achieved 99% annotation accuracy, ensuring reliable datasets for real-time safety detection.
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