DATASET

BIRDSAI

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. 

Download:

The data can be downloaded from the Labeled Information Library of Alexandria.

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)

Terms of Use:

This dataset is released under the Community Data License Agreement (permissive variant). 

Citing BIRDSAI:

If you use this dataset, please consider citing our paper: 

@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}

}