Update README.md

README.md

@@ -1,6 +1,6 @@
 # [CattleEyeView]([https://github.com/AnimalEyeQ/CattleEyeView](https://github.com/AnimalEyeQ/CattleEyeView))
 
-This is the official repository for <br/>**[[2023 VCIP] CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming]()**
+This is the official repository for <br/>**[ [2023 IEEE VCIP] CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming](https://ieeexplore.ieee.org/document/10402676)**
 <br/>**Kian Eng ONG, Sivaji RETTA, Ramarajulu SRINIVASAN, Shawn TAN, Jun LIU**
 <br/>*Information Systems Technology and Design, Singapore University of Technology and Design, Singapore*
 <br/>*[AnimalEYEQ Private Limited](https://animaleyeq.com/home)*
@@ -10,12 +10,25 @@ This is the official repository for <br/>**[[2023 VCIP] CattleEyeView: A Multi-t
 
 
 # Paper
-Coming soon
+[2023 IEEE VCIP Official Paper](https://ieeexplore.ieee.org/document/10402676)
 
+[![arXiv](https://img.shields.io/badge/arXiv-1234.56789-b31b1b.svg)](https://arxiv.org/abs/2312.08764)
 
 ## Citation
-Coming soon
-```BibTeX```
+```BibTeX
+
+@INPROCEEDINGS{10402676,
+  author={Ong, Kian Eng and Retta, Sivaji and Srinivasan, Ramarajulu and Tan, Shawn and Liu, Jun},
+  booktitle={2023 IEEE International Conference on Visual Communications and Image Processing (VCIP)}, 
+  title={CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming}, 
+  year={2023},
+  volume={},
+  number={},
+  pages={1-5},
+  keywords={Animals;Pose estimation;Cows;Multitasking;Agriculture;Task analysis;Farming;dataset;cow;detection;tracking;counting;pose estimation;instance segmentation},
+  doi={10.1109/VCIP59821.2023.10402676}}
+
+```
 
 ## Abstract
 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](https://github.com/AnimalEyeQ/CattleEyeView).