  In many sports, player re-identification is crucial for automatic video
processing and analysis. However, most of the current studies on player
re-identification in multi- or single-view sports videos focus on
re-identification in the closed-world setting using labeled image dataset, and
player re-identification in the open-world setting for automatic video analysis
is not well developed. In this paper, we propose a runner re-identification
system that directly processes single-view video to address the open-world
setting. In the open-world setting, we cannot use labeled dataset and have to
process video directly. The proposed system automatically processes raw video
as input to identify runners, and it can identify runners even when they are
framed out multiple times. For the automatic processing, we first detect the
runners in the video using the pre-trained YOLOv8 and the fine-tuned
EfficientNet. We then track the runners using ByteTrack and detect their shoes
with the fine-tuned YOLOv8. Finally, we extract the image features of the
runners using an unsupervised method with the gated recurrent unit autoencoder
and global and local features mixing. To improve the accuracy of runner
re-identification, we use shoe images as local image features and dynamic
features of running sequence images. We evaluated the system on a running
practice video dataset and showed that the proposed method identified runners
with higher accuracy than some state-of-the-art models in unsupervised
re-identification. We also showed that our proposed local image feature and
running dynamic feature were effective for runner re-identification. Our runner
re-identification system can be useful for the automatic analysis of running
videos.
