Holstein-Friesian Re-Identification using Multiple Cameras and Self-Supervision on a Working Farm

• URL: http://arxiv.org/abs/2410.12695v3
• Date: Fri, 13 Jun 2025 15:48:02 GMT
• Title: Holstein-Friesian Re-Identification using Multiple Cameras and Self-Supervision on a Working Farm
• Authors: Phoenix Yu, Tilo Burghardt, Andrew W Dowsey, Neill W Campbell,
• Abstract summary: We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle.<n>The dataset comprises 101,329 images of 90 cows, plus underlying original CCTV footage.<n>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.
• Score: 2.9391768712283772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Vision transformer-based multi-camera multi-object tracking framework for dairy cow monitoring [0.06282171844772422]
  This study developed a unique multi-camera, real-time tracking system for indoor-housed Holstein Friesian dairy cows.<n>This technology uses cutting-edge computer vision techniques, including instance segmentation and tracking algorithms to monitor cow activity seamlessly and accurately.
  arXiv  Detail & Related papers  (2025-08-03T13:36:40Z)
• Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder [48.03572115000886]
  This study introduces a fully self-supervised approach to learning robust chimpanzee face embeddings from unlabeled camera-trap footage.<n>We train Vision Transformers on automatically mined face crops, eliminating the need for identity labels.<n>This work underscores the potential of self-supervised learning in biodiversity monitoring and paves the way for scalable, non-invasive population studies.
  arXiv  Detail & Related papers  (2025-07-14T17:59:59Z)
• CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming [6.291219495092237]
  We introduce CattleEyeView dataset, the first top-down view multi-task cattle video dataset. The dataset contains 753 distinct top-down cow instances in 30,703 frames. We perform benchmark experiments to evaluate the model's performance for each task.
  arXiv  Detail & Related papers  (2023-12-14T09:18:02Z)
• Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification [12.493458478953515]
  We describe a practically evaluated approach for training visual cattle ID systems for a whole farm requiring only ten minutes of labelling effort. For the task of automatic identification of individual Holstein-Friesians in real-world farm CCTV, we show that self-supervision, metric learning, cluster analysis, and active learning can complement each other.
  arXiv  Detail & Related papers  (2022-04-22T19:41:47Z)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset [10.698921107213554]
  We publish the largest identity-annotated Holstein-Friesian cattle dataset Cows2021. We propose exploiting the temporal coat pattern appearance across videos as a self-supervision signal for animal identity learning. Results show an accuracy of Top-1 57.0% and Top-4: 76.9% and an Adjusted Rand Index: 0.53 compared to the ground truth.
  arXiv  Detail & Related papers  (2021-05-05T09:08:19Z)
• Intra-Inter Camera Similarity for Unsupervised Person Re-Identification [50.85048976506701]
  We study a novel intra-inter camera similarity for pseudo-label generation. We train our re-id model in two stages with intra-camera and inter-camera pseudo-labels, respectively. This simple intra-inter camera similarity produces surprisingly good performance on multiple datasets.
  arXiv  Detail & Related papers  (2021-03-22T08:29:04Z)
• Unsupervised Pretraining for Object Detection by Patch Reidentification [72.75287435882798]
  Unsupervised representation learning achieves promising performances in pre-training representations for object detectors. This work proposes a simple yet effective representation learning method for object detection, named patch re-identification (Re-ID) Our method significantly outperforms its counterparts on COCO in all settings, such as different training iterations and data percentages.
  arXiv  Detail & Related papers  (2021-03-08T15:13:59Z)
• Camera-aware Proxies for Unsupervised Person Re-Identification [60.26031011794513]
  This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. We propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model.
  arXiv  Detail & Related papers  (2020-12-19T12:37:04Z)
• Self-supervised Human Detection and Segmentation via Multi-view Consensus [116.92405645348185]
  We propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training. We show that our approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks.
  arXiv  Detail & Related papers  (2020-12-09T15:47:21Z)
• Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning [8.784100314325395]
  Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques.
  arXiv  Detail & Related papers  (2020-06-16T14:41:55Z)
• Automatic Detection and Recognition of Individuals in Patterned Species [4.163860911052052]
  We develop a framework for automatic detection and recognition of individuals in different patterned species. We use the recently proposed Faster-RCNN object detection framework to efficiently detect animals in images. We evaluate our recognition system on zebra and jaguar images to show generalization to other patterned species.
  arXiv  Detail & Related papers  (2020-05-06T15:29:21Z)
• Towards Precise Intra-camera Supervised Person Re-identification [54.86892428155225]
  Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view. Lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart. Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.
  arXiv  Detail & Related papers  (2020-02-12T11:56:30Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.