Video-based cattle identification and action recognition
This enables automated farm provenance and welfare monitoring for livestock farmers, though it's an incremental application of existing deep learning methods to a new domain.
The researchers tackled automated monitoring of cow welfare by developing deep learning models for cattle identification and action recognition from farm videos, achieving 81.2% precision for identification, 84.4% accuracy for drinking detection, and 94.4% accuracy for grazing detection.
We demonstrate a working prototype for the monitoring of cow welfare by automatically analysing the animal behaviours. Deep learning models have been developed and tested with videos acquired in a farm, and a precision of 81.2\% has been achieved for cow identification. An accuracy of 84.4\% has been achieved for the detection of drinking events, and 94.4\% for the detection of grazing events. Experimental results show that the proposed deep learning method can be used to identify the behaviours of individual animals to enable automated farm provenance. Our raw and ground-truth dataset will be released as the first public video dataset for cow identification and action recognition. Recommendations for further development are also provided.