Few-Shot Image Recognition for UAV Sports Cinematography

Emmanouil Patsiouras, Anastasios Tefas, Ioannis Pitas; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 238-239

The goal of few-shot image learning is to utilize a very small amount of training examples in order to train a machine learning model to recognize a given number of image classes. While humans can perform such a task pretty much effortlessly, applying the same mechanism to deep learning visual recognition systems is a much more difficult task, having a wide range of real-world visual recognition applications. In this paper, we investigate the behavior of such few-shot methods in the context of drone vision cinematography for sports event filming, in order to recognize new image classes by taking into consideration the fact that this new class we wish to identify is a subclass of an already known class. More specifically we use UAV footage to recognize certain types of athletes, belonging to a subset of an original athlete class, utilizing only a handful of recorded images of this athlete subclass. We examine the effects of such methods on image recognition accuracy while proposing a novel approach for accuracy optimizations. The overall task is evaluated on actual cycling race UAV footage.