BIRDSAI Computer Vision Project
BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos
Authors:
- Elizabeth Bondi, Harvard University
- Raghav Jain, University of Southern California
- Palash Aggrawal, Indraprastha Institute of Information Technology
- Saket Anand, Indraprastha Institute of Information Technology
- Robert Hannaford, Duke University
- Ashish Kapoor, University of Delhi
- Jim Piavis, The Citadel
- Shital Shah, University of Mumbai
- Lucas Joppa, Chief Environmental Officer, Microsoft
- Bistra Dilkina, University of Southern California
- Milind Tambe, Harvard University
Published: 2020
Description: The Benchmarking IR Dataset for Surveillance with Aerial Intelligence (BIRDSAI, pronounced bird's-eye) is a long-wave thermal infrared dataset containing nighttime images of animals and humans in Southern Africa. The dataset allows for benchmarking of algorithms for automatic detection and tracking of humans and animals with both real and synthetic videos.
Use Cases: Wildlife Poaching Prevention, Night-time Intruder Detection, Wildlife Monitoring, Animal Behavior Research, Long Distance IR Detection
Download: The data can be downloaded from the Labeled Information Library of Alexandria
Training Dataset Download: https://lilablobssc.blob.core.windows.net/conservationdrones/v01/conservation_drones_train_real.zip
Annotation Format:
We follow the MOT annotation format, which is a CSV with the following columns:
<frame_number>, <object_id>, <x>, <y>, <w>, <h>, <class>, <species>, <occlusion>, <noise>
class: 0 if animals, 1 if humans
species: between -1 and 8 representing species below; 3 and 4 occur only in real data; 5, 6, 7, 8 occur only in synthetic data (note: most very small objects have unknown species)
-1: unknown, 0: human, 1: elephant, 2: lion, 3: giraffe, 4: dog, 5: crocodile, 6: hippo, 7: zebra, 8: rhino
occlusion: 0 if there is no occlusion, 1 if there is an occlusion (i.e., either occluding or occluded) (note: intersection over union threshold of 0.3 used to assign occlusion; more details in paper)
noise: 0 if there is no noise, 1 if there is noise (note: noise labels were interpolated from object locations in previous and next frames; for more than 4 consecutive frames without labels, no noise labels were included; more details in paper)
Acknowledgements: BIRDSAI was supported by Microsoft AI for Earth, NSF CCF-1522054 and IIS-1850477, MURI W911NF-17-1-0370, and the Infosys Center for Artificial Intelligence, IIIT-Delhi . Thanks to the labeling team and the Labeled Information Library of Alexandria for hosting the data.
Citation:
@inproceedings{bondi2020birdsai,
title={BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos},
author={Bondi, Elizabeth and Jain, Raghav and Aggrawal, Palash and Anand, Saket and Hannaford, Robert and Kapoor, Ashish and Piavis, Jim and Shah, Shital and Joppa, Lucas and Dilkina, Bistra and Tambe, Milind},
booktitle={WACV},
year={2020}
}
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
Cite this Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ birdsai-g4m8o_dataset,
title = { BIRDSAI Dataset },
type = { Open Source Dataset },
author = { Tyler },
howpublished = { \url{ https://universe.roboflow.com/tyler-wbpfp/birdsai-g4m8o } },
url = { https://universe.roboflow.com/tyler-wbpfp/birdsai-g4m8o },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { sep },
note = { visited on 2023-01-31 },
}