4.7 Article

Tracking individual broilers on video in terms of time and distance

Related references

Note: Only part of the references are listed.
Article Agriculture, Dairy & Animal Science

Passive radio frequency identification and video tracking for the determination of location and movement of broilers

J. E. Doornweerd et al.

Summary: Phenotyping individual animals can be challenging and time-consuming, especially for traits related to health and performance. However, individual broiler behavior can serve as a reliable proxy for these traits when recorded automatically using sensors. In this study, a comparison was made between a passive RFID system and video tracking for determining the location and movement of broilers.

POULTRY SCIENCE (2023)

Article Agriculture, Dairy & Animal Science

Tracking and Characterizing Spatiotemporal and Three-Dimensional Locomotive Behaviors of Individual Broilers in the Three-Point Gait-Scoring System

Guoming Li et al.

Summary: This research aimed to track and characterize the locomotive behaviors of individual broilers using three gait scores commonly used in the U.S. broiler industry. Results showed that broilers with lower gait scores exhibited more obvious lateral body oscillation patterns and moved significantly or numerically faster. Deep-learning algorithms, depth sensing, and image processing were used to analyze videos and images, providing an automated and objective method for gait scoring broilers.

ANIMALS (2023)

Article Agriculture, Dairy & Animal Science

Broiler Mobility Assessment via a Semi-Supervised Deep Learning Model and Neo-Deep Sort Algorithm

Mustafa Jaihuni et al.

Summary: The researchers used a combination of artificial intelligence methods and computer algorithms to track individual chickens, which provided more accurate measurements of their mobility compared to traditional methods. This combined model could provide real-time and accurate information.

ANIMALS (2023)

Proceedings Paper Computer Science, Artificial Intelligence

Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space

Fan Yang et al.

Summary: We propose a Cascaded Buffered IoU (C-BIoU) tracker for tracking multiple objects with irregular motions and indistinguishable appearances. The C-BIoU tracker expands the matching space by adding buffers to mitigate the effect of irregular motions. The tracker achieves state-of-the-art results on MOT datasets focusing on irregular motions and indistinguishable appearances.

2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV) (2023)

Proceedings Paper Computer Science, Artificial Intelligence

Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking

Jinkun Cao et al.

Summary: This paper introduces a Kalman filter-based method for multi-object tracking, and addresses the issue of inaccurate linear motion estimation over prolonged periods. The authors propose an observation-centric approach to improve tracking performance by fixing the accumulated error in filter parameters. The method achieves state-of-the-art results on multiple datasets.

2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2023)

Proceedings Paper Computer Science, Artificial Intelligence

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Chien-Yao Wang et al.

Summary: Real-time object detection is an important research topic in computer vision, and the development of new approaches in architecture optimization and training optimization has led to two related research topics. To address these topics, a trainable solution combining flexible and efficient training tools, proposed architecture, and compound scaling method is proposed. YOLOv7 outperforms all known object detectors in terms of speed and accuracy, achieving the highest AP accuracy of 56.8% among real-time object detectors with 30 FPS or higher on GPU V100.

2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR (2023)

Article Agriculture, Dairy & Animal Science

Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine learning

Bo-Lin Chen et al.

Summary: This study proposes an automatic warning system for monitoring anomalous dispersion and movement of chicken flocks in commercial chicken farms. By using video and image processing techniques, the system detects the position and movement of chicken flocks based on established normal ranges. For example, when the dispersion or movement values are not within the normal range, the system alerts farmers to check the chicken farm, saving labor time and reducing risk.

POULTRY SCIENCE (2023)

Article Agriculture, Dairy & Animal Science

Implementation of Inertia Sensor and Machine Learning Technologies for Analyzing the Behavior of Individual Laying Hens

Sayed M. Derakhshani et al.

Summary: This study demonstrates the feasibility of analyzing the behaviors of laying hens using wearable inertia sensor technology and machine learning. The results show that the model can accurately classify different behaviors and support farmers in managing hens in loose housing systems.

ANIMALS (2022)

Article Computer Science, Artificial Intelligence

HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

Jonathon Luiten et al.

Summary: The higher order tracking accuracy (HOTA) is a novel evaluation metric for multi-object tracking that balances accurate detection, association, and localization. It decomposes into sub-metrics to evaluate different error types separately, providing clear analysis of tracking performance. The HOTA scores align better with human visual evaluation of tracking performance compared to established metrics.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2021)

Article Agriculture, Dairy & Animal Science

Classification of broiler behaviours using triaxial accelerometer and machine learning

X. Yang et al.

Summary: Understanding broiler behaviours through wearable accelerometers and machine learning models can effectively classify specific behaviors, with varying performances of different models. A window length of 1 second yields the best performance for classifying continuous broiler behaviors.

ANIMAL (2021)

Article Agriculture, Dairy & Animal Science

Individual Detection and Tracking of Group Housed Pigs in Their Home Pen Using Computer Vision

Lisette. E. van der Zande et al.

Summary: Modern welfare definitions require animals to meet the Five Freedoms and be able to adapt to changes and experience positive states. This study investigated the potential of using state-of-the-art CV algorithms to track individual pig activity, finding that a combined detection model performed best. The tracking algorithm performed better in enriched environments compared to barren environments.

FRONTIERS IN ANIMAL SCIENCE (2021)

Review Ecology

Optimizing the use of biologgers for movement ecology research

Hannah J. Williams et al.

JOURNAL OF ANIMAL ECOLOGY (2020)

Article Computer Science, Artificial Intelligence

Real-time behavior detection and judgment of egg breeders based on YOLO v3

Juan Wang et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Agriculture, Dairy & Animal Science

Validation of an Ultra-Wideband Tracking System for Recording Individual Levels of Activity in Broilers

Malou van der Sluis et al.

ANIMALS (2019)

Article Multidisciplinary Sciences

Energy allocation and behaviour in the growing broiler chicken

Peter G. Tickle et al.

SCIENTIFIC REPORTS (2018)

Article Agricultural Engineering

Implementation of an automatic 3D vision monitor for dairy cow locomotion in a commercial farm

Tom Van Hertem et al.

BIOSYSTEMS ENGINEERING (2018)

Article Agricultural Engineering

Predicting broiler gait scores from activity monitoring and flock data

Tom Van Hertem et al.

BIOSYSTEMS ENGINEERING (2018)

Article Robotics

Now You See Me: Convolutional Neural Network Based Tracker for Dairy Cows

Oleksiy Guzhva et al.

FRONTIERS IN ROBOTICS AND AI (2018)

Article Engineering, Electrical & Electronic

Smart RFID Antenna System for Indoor Tracking and Behavior Analysis of Small Animals in Colony Cages

Luca Catarinucci et al.

IEEE SENSORS JOURNAL (2014)