Neill W Campbell

Phoenix Yu, Tilo Burghardt, Andrew W Dowsey et al.

Holstein-Friesian detection and re-identification (Re-ID) methods capture individuals well when targets are spatially separate. However, existing approaches, including YOLO-based species detection, break down when cows group closely together. This is particularly prevalent for species which have outline-breaking coat patterns. To boost both effectiveness and transferability in this setting, we propose a new detect-segment-identify pipeline that leverages the Open-Vocabulary Weight-free Localisation and the Segment Anything models as pre-processing stages alongside Re-ID networks. To evaluate our approach, we publish a collection of nine days CCTV data filmed on a working dairy farm. Our methodology overcomes detection breakdown in dense animal groupings, resulting in a 98.93% accuracy. This significantly outperforms current oriented bounding box-driven, as well as SAM species detection baselines with accuracy improvements of 47.52% and 27.13%, respectively. We show that unsupervised contrastive learning can build on this to yield 94.82% Re-ID accuracy on our test data. Our work demonstrates that Re-ID in crowded scenarios is both practical as well as reliable in working farm settings with no manual intervention. Code and dataset are provided for reproducibility.

Phoenix Yu, Tilo Burghardt, Andrew W Dowsey et al.

We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over seven days on a working dairy farm, the dataset comprises 101,329 images of 90 cows, plus underlying original CCTV footage. The dataset is provided with full computer vision recognition baselines, that is both a supervised and self-supervised learning framework for individual cow identification trained on cattle tracklets. We report a performance above 96% single image identification accuracy from the dataset and demonstrate that combining data from multiple cameras during learning enhances self-supervised identification. We show that our framework enables automatic cattle identification, barring only the simple human verification of tracklet integrity during data collection. Crucially, our study highlights that multi-camera, supervised and self-supervised components in tandem not only deliver highly accurate individual cow identification, but also achieve this efficiently with no labelling of cattle identities by humans. We argue that this improvement in efficacy has practical implications for livestock management, behaviour analysis, and agricultural monitoring. For reproducibility and practical ease of use, we publish all key software and code including re-identification components and the species detector with this paper, available at https://tinyurl.com/MultiCamCows2024.

Huimin Liu, Jing Gao, Daria Baran et al.

Cattle behaviour is a crucial indicator of an individual animal health, productivity and overall well-being. Video-based monitoring, combined with deep learning techniques, has become a mainstream approach in animal biometrics, and it can offer high accuracy in some behaviour recognition tasks. We present Cattle-CLIP, a multimodal deep learning framework for cattle behaviour recognition, using semantic cues to improve the performance of video-based visual feature recognition. It is adapted from the large-scale image-language model CLIP by adding a temporal integration module. To address the domain gap between web data used for the pre-trained model and real-world cattle surveillance footage, we introduce tailored data augmentation strategies and specialised text prompts. Cattle-CLIP is evaluated under both fully-supervised and few-shot learning scenarios, with a particular focus on data-scarce behaviour recognition - an important yet under-explored goal in livestock monitoring. To evaluate the proposed method, we release the CattleBehaviours6 dataset, which comprises six types of indoor behaviours: feeding, drinking, standing-self-grooming, standing-ruminating, lying-self-grooming and lying-ruminating. The dataset consists of 1905 clips collected from our John Oldacre Centre dairy farm research platform housing 200 Holstein-Friesian cows. Experiments show that Cattle-CLIP achieves 96.1% overall accuracy across six behaviours in a supervised setting, with nearly 100% recall for feeding, drinking and standing-ruminating behaviours, and demonstrates robust generalisation with limited data in few-shot scenarios, highlighting the potential of multimodal learning in agricultural and animal behaviour analysis.