Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and Action Recognition in Drone Imagery
<|reference_start|>Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and Action Recognition in Drone Imagery: In this article, we explore the potential of zero-shot Large Multimodal Models (LMMs) in the domain of drone perception. We focus on person detection and action recognition tasks and evaluate two prominent LMMs, namely YOLO-World and GPT-4V(ision) using a publicly available dataset captured from aerial views. Traditional deep learning approaches rely heavily on large and high-quality training datasets. However, in certain robotic settings, acquiring such datasets can be resource-intensive or impractical within a reasonable timeframe. The flexibility of prompt-based Large Multimodal Models (LMMs) and their exceptional generalization capabilities have the potential to revolutionize robotics applications in these scenarios. Our findings suggest that YOLO-World demonstrates good detection performance. GPT-4V struggles with accurately classifying action classes but delivers promising results in filtering out unwanted region proposals and in providing a general description of the scenery. This research represents an initial step in leveraging LMMs for drone perception and establishes a foundation for future investigations in this area.<|reference_end|>
@article{limberg2024leveraging,
    title={Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and
  Action Recognition in Drone Imagery},
    author={Christian Limberg, Artur Gonc{c}alves, Bastien Rigault, Helmut
  Prendinger},
    journal={arXiv preprint arXiv:2404.01571},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01571},
    primaryClass={cs.CV cs.RO}
}