Cattle-dataset

Introduction

Our proposed dataset is large and contains a variety of scenes and lighting conditions that can better represent the real situation of the pasture. Expressly, We set up smart cameras in several pastures of Yibin and Qinghai. It took five months to monitor the daily life of cattle in different periods, seasons, scenarios and weather conditions to get video data. The intercepted surveillance video is sampled in real-time and then selected. Data with diverse scenes and significant variations in cattle movement were picked into the dataset.

Data

The format of the file is as follows：

data
 │  
 ├─test
 │   ├─ground_truth  // store the mat file of the corresponding image
 │   └─images
 │
 └─train
     ├─ground_truth
     └─images

Statistics of different crowd counting datasets and cattle dataset as follows:

Dataset	#Train	#Test	Avg. Resolution (H × W)	Count Statistics
				Total	Min	Ave	Max
UCSD	800	1,200	158 × 238	49,885	11	25	46
UCF_CC_50	-	50	2101 × 2888	63,974	94	1,279	4,543
WorldExpo	3,380	600	576 × 720	199,923	1	50	253
ShanghaiTech_A	300	182	589 × 868	241,677	33	501	3,139
ShanghaiTech_B	400	316	768 × 1024	88,488	9	123	578
Cattle dataset	493	357	864 × 1317	18,403	3	22	129

Citation

If you use our dataset in your research, please cite with:

@article{math10203856,
  author = {Zhong, Minyue and Tan, Yao and Li, Jie and Zhang, Hongming and Yu, Siyi},
  title = {Cattle Number Estimation on Smart Pasture Based on Multi-Scale Information Fusion},
  journal = {Mathematics},
  volume = {10},
  year = {2022},
  number = {20},
}