activity
20202023
most citedDeep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation

63 citations · 89 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2023★ 5 cited

CVB: A Video Dataset of Cattle Visual Behaviors

Ali Zia, Renuka Sharma, Reza Arablouei +7

Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the u…

cs.CV2022

Point-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Clouds

Ziwei Wang, Reza Arablouei, Jiajun Liu +3

Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving. However, labeling point cloud data is labor-intensive as it re…

cs.LG2022★ 13 cited

In-situ animal behavior classification using knowledge distillation and fixed-point quantization

Reza Arablouei, Liang Wang, Caitlin Phillips +3

We explore the use of knowledge distillation (KD) for learning compact and accurate models that enable classification of animal behavior from accelerometry data on wearable devices…

cs.LG2022★ 5 cited

Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data

Reza Arablouei, Ziwei Wang, Greg J. Bishop-Hurley +1

In this paper, we examine the use of data from multiple sensing modes, i.e., accelerometry and global navigation satellite system (GNSS), for classifying animal behavior. We extrac…

cs.LG2021★ 3 cited

Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks

Liang Wang, Reza Arablouei, Flavio A. P. Alvarenga +1

We study the classification of animal behavior using accelerometry data through various recurrent neural network (RNN) models. We evaluate the classification performance and comple…

cs.CV2021

Video-based cattle identification and action recognition

Chuong Nguyen, Dadong Wang, Karl Von Richter +3

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 vide…

cs.LG2020★ 63 cited

Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation

Seyedeh Faezeh Hosseini Noorbin, Siamak Layeghy, Brano Kusy +3

Automated cattle activity classification allows herders to continuously monitor the health and well-being of livestock, resulting in increased quality and quantity of beef and dair…