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Video Internet of Things (VIoT) has shown full potential in collecting anunprecedented volume of video data. Learning to schedule perceiving models andanalyzing the collected videos intelligently will be potential sparks for VIoT.In this paper, to address the challenges posed by the fine-grained andinterrelated vision tool usage of VIoT, we build VIoTGPT, the framework basedon LLMs to correctly interact with humans, query knowledge videos, and invokevision models to accomplish complicated tasks. To support VIoTGPT and relatedfuture works, we meticulously crafted the training dataset and establishedbenchmarks involving 11 representative vision models across three categoriesbased on semi-automatic annotations. To guide LLM to act as the intelligentagent towards intelligent VIoT, we resort to ReAct instruction tuning based onthe collected VIoT dataset to learn the tool capability. Quantitative andqualitative experimental results and analyses demonstrate the effectiveness ofVIoTGPT.

VIoTGPT: Learning to Schedule Vision Tools towards Intelligent Video Internet of Things