Video-based cattle identification and action recognition

• URL: http://arxiv.org/abs/2110.07103v1
• Date: Thu, 14 Oct 2021 00:55:56 GMT
• Title: Video-based cattle identification and action recognition
• Authors: Chuong Nguyen, Dadong Wang, Karl Von Richter, Philip Valencia, Flavio A. P. Alvarenga, Gregory Bishop-Hurley
• Abstract summary: 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.
• Score: 0.910162205512083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MultiCamCows2024 -- A Multi-view Image Dataset for AI-driven Holstein-Friesian Cattle Re-Identification on a Working Farm [2.9391768712283772]
  We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle. The dataset comprises 101, 329 images of 90 cows, plus the underlying original CCTV footage. We show that our framework enables fully automatic cattle identification, barring only the simple human verification of tracklet integrity.
  arXiv  Detail & Related papers  (2024-10-16T15:58:47Z)
• Distillation-guided Representation Learning for Unconstrained Gait Recognition [50.0533243584942]
  We propose a framework, termed GAit DEtection and Recognition (GADER), for human authentication in challenging outdoor scenarios. GADER builds discriminative features through a novel gait recognition method, where only frames containing gait information are used. We evaluate our method on multiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets.
  arXiv  Detail & Related papers  (2023-07-27T01:53:57Z)
• CVB: A Video Dataset of Cattle Visual Behaviors [13.233877352490923]
  Existing datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. We introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle.
  arXiv  Detail & Related papers  (2023-05-26T00:44:11Z)
• A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors [56.554277096170246]
  We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-the-wild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels.
  arXiv  Detail & Related papers  (2023-05-15T16:02:56Z)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)
• Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video [8.906235809404189]
  Early lameness detection helps farmers address illnesses early and avoid negative effects caused by the degeneration of cows' condition. We collected a dataset of short clips of cows exiting a milking station and annotated the degree of lameness of the cows. We proposed a lameness detection method that leverages pre-trained neural networks to extract discriminative features from videos and assign a binary score to each cow indicating its condition: "healthy" or "lame"
  arXiv  Detail & Related papers  (2022-06-09T12:16:31Z)
• RealGait: Gait Recognition for Person Re-Identification [79.67088297584762]
  We construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner. Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.
  arXiv  Detail & Related papers  (2022-01-13T06:30:56Z)
• 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)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• Dairy Cow rumination detection: A deep learning approach [0.8312466807725921]
  Rumination behavior is a significant variable for tracking the development and yield of animal husbandry. Modern attached devices are invasive, stressful and uncomfortable for the cattle. In this study, we introduce an innovative monitoring method using Convolution Neural Network (CNN)-based deep learning models.
  arXiv  Detail & Related papers  (2021-01-07T07:33:32Z)
• 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)

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.