Dairy cow re-identification and tracking using computer vision

Improving the monitoring of health and well-being of dairy cows through the use of computer vision based systems is a topic of ongoing research. A reliable and low-cost method for identifying cow individuals would enable automatic detection of stress, sickness or injury, and the daily observation of the animals would be made easier. Neural networks have been used successfully in the identification of cow individuals, but methods are needed that do not require incessant annotation work to generate training datasets when there are changes within a group. Methods for person re-identification and tracking have been researched extensively, with the aim of generalizing beyond the training set. These methods have been found suitable also for re-identifying and tracking previously unseen dairy cows in video frames. In this thesis, a metric-learning based re-identification model pre-trained on an existing cow dataset is compared to a similar model that has been trained on new video data recorded at Luke Maaninka research farm in Spring 2021, which contains 24 individually labelled cow individuals. The models are evaluated in tracking context as appearance descriptors in Kalman filter based tracking algorithm. The test data is video footage from a separate enclosure in Maaninka and a group of 24 previously unseen cow individuals. In addition, a simple procedure is proposed for the automatic labeling of cow identities in images based on RFID data collected from cow ear tags and feeding stations, and the known feeding station locations