arXiv:2611.15601 · v1
Cat Royale Comprehensive Dataset: A Dataset for Pose Detection, Segmentation, Animal Action Recognition and Scene Understanding
摘要
Animal focused computer vision datasets are of a growing interest in the fields of both Computer Vision and Animal behaviour. The ability to make sense of complex scenes via a variety of Computer Vision tasks has substantial implications for behavioural understanding and welfare. The perfection of these tasks will help lead to better health and emotional outcomes for wild and domestic animals. In this work we present the Cat Royale Comprehensive Dataset (CRCD), a large, comprehensive, multi-modal dataset for feline understanding, based on a huge database of over 500 hours of video captured during the Cat Royale artwork and research project. Our dataset contains annotations for a range of computer vision problems including 20,000 images with instance segmentation masks, 5000 images with multi-subject pose coordinates, over 800 video clips with action classes, and over 800 clips with detailed text description. This is the first such dataset to link this wide range of annotation types together for multi-modal use. We release this entire dataset, and our custom annotation tools will also be made available, in order to allow for ongoing expansion. We also provide a series of benchmarks for the tasks of Instance Segmentation, Pose detection, Temporal Action Localisation, and finally video-text retrieval. The totality of this multi-modal dataset is the data needed to solve both these individual computer vision tasks as well as enabling more complex multi-modal tasks making use of a combination of annotations.
作者备注：26 pages, 4 figures, 6 tables. Submitted to the International Journal of Computer Vision. Dataset: https://doi.org/10.5281/zenodo.19498943