A Self-Supervised Method for Body Part Segmentation and Keypoint Detection of Rat Images
<|reference_start|>A Self-Supervised Method for Body Part Segmentation and Keypoint Detection of Rat Images: Recognition of individual components and keypoint detection supported by instance segmentation is crucial to analyze the behavior of agents on the scene. Such systems could be used for surveillance, self-driving cars, and also for medical research, where behavior analysis of laboratory animals is used to confirm the aftereffects of a given medicine. A method capable of solving the aforementioned tasks usually requires a large amount of high-quality hand-annotated data, which takes time and money to produce. In this paper, we propose a method that alleviates the need for manual labeling of laboratory rats. To do so, first, we generate initial annotations with a computer vision-based approach, then through extensive augmentation, we train a deep neural network on the generated data. The final system is capable of instance segmentation, keypoint detection, and body part segmentation even when the objects are heavily occluded.<|reference_end|>
@article{kopácsi2024a,
    title={A Self-Supervised Method for Body Part Segmentation and Keypoint
  Detection of Rat Images},
    author={L'aszl'o Kop'acsi, 'Aron F'othi, Andr'as LH{o}rincz},
    journal={arXiv preprint arXiv:2405.04650},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04650},
    primaryClass={cs.CV cs.AI}
}