Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - CattleEyeView
T2 - 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023
AU - Ong, Kian Eng
AU - Retta, Sivaji
AU - Srinivasan, Ramarajulu
AU - Tan, Shawn
AU - Liu, Jun
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2024/1/29
Y1 - 2024/1/29
N2 - Cattle farming is one of the important and profitable agricultural industries. Employing intelligent automated precision livestock farming systems that can count animals, track the animals and their poses will raise productivity and significantly reduce the heavy burden on its already limited labor pool. To achieve such intelligent systems, a large cattle video dataset is essential in developing and training such models. However, many current animal datasets are tailored to few tasks or other types of animals, which result in poorer model performance when applied to cattle. Moreover, they do not provide top-down views of cattle. To address such limitations, we introduce CattleEyeView dataset, the first top-down view multi-Task cattle video dataset for a variety of inter-related tasks (i.e., counting, detection, pose estimation, tracking, instance segmentation) that are useful to count the number of cows and assess their growth and well-being. The dataset contains 753 distinct top-down cow instances in 30,703 frames (14 video sequences). We perform benchmark experiments to evaluate the model's performance for each task. The dataset and codes can be found at https://github.com/AnimalEyeQ/CattleEyeView
AB - Cattle farming is one of the important and profitable agricultural industries. Employing intelligent automated precision livestock farming systems that can count animals, track the animals and their poses will raise productivity and significantly reduce the heavy burden on its already limited labor pool. To achieve such intelligent systems, a large cattle video dataset is essential in developing and training such models. However, many current animal datasets are tailored to few tasks or other types of animals, which result in poorer model performance when applied to cattle. Moreover, they do not provide top-down views of cattle. To address such limitations, we introduce CattleEyeView dataset, the first top-down view multi-Task cattle video dataset for a variety of inter-related tasks (i.e., counting, detection, pose estimation, tracking, instance segmentation) that are useful to count the number of cows and assess their growth and well-being. The dataset contains 753 distinct top-down cow instances in 30,703 frames (14 video sequences). We perform benchmark experiments to evaluate the model's performance for each task. The dataset and codes can be found at https://github.com/AnimalEyeQ/CattleEyeView
KW - counting
KW - cow
KW - dataset
KW - detection
KW - instance segmentation
KW - pose estimation
KW - tracking
U2 - 10.1109/VCIP59821.2023.10402676
DO - 10.1109/VCIP59821.2023.10402676
M3 - Conference contribution/Paper
AN - SCOPUS:85184852633
T3 - 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023
BT - 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP)
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 December 2023 through 7 December 2023
ER -