Following recipes while cooking is an important but difficult task for visually impaired individuals. We developed OSCAR (Object Status Context Awareness for Recipes), a novel approach that provides recipe progress tracking and context-aware feedback on the completion of cooking tasks through tracking object statuses. OSCAR leverages both Large-Language Models (LLMs) and Vision-Language Models (VLMs) to manipulate recipe steps, extract object status information, align visual frames with object status, and provide cooking progress tracking log. We evaluated OSCAR's recipe following functionality using 173 YouTube cooking videos and 12 real-world non-visual cooking videos to demonstrate OSCAR's capability to track cooking steps and provide contextual guidance. Our results highlight the effectiveness of using object status to improve performance compared to baseline by over 20% across different VLMs, and we present factors that impact prediction performance. Furthermore, we contribute a dataset of real-world non-visual cooking videos with step annotations as an evaluation benchmark.
View on arXiv@article{li2025_2503.05962,
title={ OSCAR: Object Status and Contextual Awareness for Recipes to Support Non-Visual Cooking },
author={ Franklin Mingzhe Li and Kaitlyn Ng and Bin Zhu and Patrick Carrington },
journal={arXiv preprint arXiv:2503.05962},
year={ 2025 }
}