Data-driven feature extraction is examined to realize accurate and robust calving detection. Automatic calving sign detection systems can support farmers' decision making. In this paper, neural networks are designed to extract information relevant to calving signs, which can be observed from video frames, such as the frequency in pre-calving postures, statistics in movement, and statistics in rotation. Experimental comparisons using surveillance videos demonstrate that the proposed feature extraction methods contribute to reducing false positives and explaining the basis of the prediction compared to the end-to-end calving detection system.
|Title of host publication
|Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2020
|25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: 2021 Jan 10 → 2021 Jan 15
|Proceedings - International Conference on Pattern Recognition
|25th International Conference on Pattern Recognition, ICPR 2020
|21/1/10 → 21/1/15
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition