Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effects of microbial inoculants and planting density on soybean summer-sown growth, nutrients accumulation and yield in Southern Xinjiang.

Frontiers in plant science·2026
Same author

SFRP2 drives aerobic glycolysis and tumor progression in ovarian cancer by transcriptional upregulation of PTK2B.

Journal of translational medicine·2026
Same author

[Research Progress on the Impacts and Health Effects of Combined Contamination of Microplastics and Cadmium in Soils].

Huan jing ke xue= Huanjing kexue·2026
Same author

Fulminant Aeromonas caviae bacteremia with migratory myalgia: a case report of a CTX-M-3-producing multidrug-resistant strain.

BMC infectious diseases·2026
Same author

Inhibition of GLUT1 Ameliorates Thickening of the Glomerular Basement Membrane via the Rheb/mTORC1 Pathway in Diabetic Nephropathy.

Diabetes·2026
Same author

Investigation of the Structural Characteristics and Electrical Conductivity of Natural and Synthetic Coal-Derived Graphitic Carbon.

ACS omega·2026
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2025

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.3K

A new dataset for video-based cow behavior recognition.

Kuo Li1, Daoerji Fan2, Huijuan Wu1

  • 1College of Electronic Information Engineering, Inner Mongolia University, College Road No. 235, Hohhot, 010021, Inner Mongolia Autonomous Region, China.

Scientific Reports
|August 12, 2024
PubMed
Summary
This summary is machine-generated.

A new dataset, CBVD-5, enables cow behavior recognition, including standing, lying, foraging, rumination, and drinking. A baseline model achieved a 21.28% error rate, advancing dairy intelligence.

Keywords:
Dairy cow behavior recognitionDatasetSlowFast modelVideo sequence

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.4K

Related Experiment Videos

Last Updated: Jun 17, 2025

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.3K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.4K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Animal Behavior

Background:

  • Accurate monitoring of dairy cow behavior is crucial for health, welfare, and productivity.
  • Existing datasets often lack diversity in behaviors, lighting conditions, or scale.
  • Standardized, comprehensive datasets are needed to advance automated behavior recognition systems.

Purpose of the Study:

  • Introduce the Cow Behavior Video Dataset 5 (CBVD-5), a novel, large-scale, video-based dataset for multi-behavior recognition in cows.
  • Provide a standardized resource for developing and evaluating AI models for dairy cow behavior analysis.
  • Facilitate advancements in precision agriculture and animal welfare monitoring.

Main Methods:

  • Collected 96 hours of video data from 107 cows across various lighting conditions, including nighttime.
  • Utilized seven cameras to capture footage, resulting in 687 video segments and 206,100 images.
  • Annotated five key cow behaviors (standing, lying, foraging, rumination, drinking) using the VIA web tool by domain experts.

Main Results:

  • Developed a SlowFast cow multi-behavior recognition model as a baseline, achieving an error rate of 21.28% on the test set.
  • Demonstrated the model's effectiveness in learning category labels from the dataset's behavior data.
  • Validated the dataset's utility for tasks beyond behavior recognition, such as cow target detection.

Conclusions:

  • The CBVD-5 dataset is a significant contribution to dairy cow behavior recognition, offering a rich resource for research and development.
  • The dataset will advance agricultural intelligence, improve dairy cow health and welfare monitoring, and support educational initiatives.
  • CBVD-5 will be made freely available to researchers globally to foster innovation in the field.