Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and Action Recognition in Drone Imagery

Christian Limberg, Artur Gonçalves, Bastien Rigault, Helmut Prendinger

Keywords: Large Multimodal Models, YOLO-World, GPT-4V, Drone Perception, Person Detection

TL;DR: We investigate the potential of zero-shot Large Multimodal Models in the domain of person detection from drones.

Abstract:

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.

Submission Number: 47