README.md

---
license: cc0-1.0
task_categories:
- video-classification
tags:
- zebra
- giraffe
- plains zebra
- Grevy's zebra
- video
- animal behavior
- behavior recognition
- annotation
- annotated video
- conservation
- drone
- UAV
- imbalanced
- Kenya
- Mpala Research Centre
pretty_name: >-
  KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone
  Videos
size_categories:
- 1M<n<10M
---
# Dataset Card for KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone Videos

## Dataset Description

- **Homepage:** https://dirtmaxim.github.io/kabr/
- **Repository:** https://github.com/dirtmaxim/kabr-tools
- **Paper:** [Coming Soon]
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

We present a novel high-quality dataset for animal behavior recognition from drone videos. 
The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. 
The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. 
In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. 
Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. 
The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors.

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### Supported Tasks and Leaderboards

[Include Benchmarks Here]

### Languages

[More Information Needed]

## Dataset Structure

The KABR dataset follows the Charades format:

```
KABR
    /images
        /video_1
            /image_1.jpg
            /image_2.jpg
            ...
            /image_n.jpg
        /video_2
            /image_1.jpg
            /image_2.jpg
            ...
            /image_n.jpg
        ...
        /video_n
            /image_1.jpg
            /image_2.jpg
            /image_3.jpg
            ...
            /image_n.jpg
    /annotation
        /classes.json
        /train.csv
        /val.csv
```

The dataset can be directly loaded and processed by the [SlowFast](https://github.com/facebookresearch/SlowFast) framework.

**Informational Files**
* `KABR/configs`: examples of SlowFast framework configs.
* `KABR/annotation/distribution.xlsx`: distribution of classes for all videos.

**Scripts:**
* `image2video.py`: Encode image sequences into the original video.
  * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `video/G0067.mp4`.
* `image2visual.py`: Encode image sequences into the original video with corresponding annotations.
  * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `visual/G0067.mp4`.

### Data Instances

[More Information Needed]
**Naming:** Within the image folder, the `video_n` folders are named as follows (X indicates a number):
* G0XXX.X - Giraffes
* ZP0XXX.X - Plains Zebras
* ZG0XXX.X - Grevy's Zebras
* Within each of these folders the images are simply `X.jpg`.

### Data Fields

[More Information Needed]

### Data Splits

Training and validation sets are indicated by their respecive CSV files, located within the `annotation` folder.

## Dataset Creation

### Curation Rationale

We present a novel high-quality dataset for animal behavior recognition from drone videos. 
The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. 
The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. 
In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. 
Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. 
The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors. 
We believe that this dataset will be a valuable resource for researchers working on animal behavior recognition, as it provides a diverse and high-quality set of annotated videos that can be used for evaluating deep learning models. 
Additionally, the dataset can be used to study the behavior patterns of Kenyan animals and can help to inform conservation efforts and wildlife management strategies. 

[To be added:]
We provide a detailed description of the dataset and its annotation process, along with some initial experiments on the dataset using conventional deep learning models. 
The results demonstrate the effectiveness of the dataset for animal behavior recognition and highlight the potential for further research in this area.

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

Data was collected from 6 January 2023 through 21 January 2023 at the [Mpala Research Centre](https://mpala.org/) in Kenya.

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process.
Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations.

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

<!---location for authors instead of under curators?--->
* Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957
* Jenna Kline (The Ohio State University)
* Michelle Ramirez (The Ohio State University)
* Sam Stevens (The Ohio State University)
* Alec Sheets (The Ohio State University) - ORCID: 0000-0002-3737-1484
* Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347
* Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010
* Elizabeth Campolongo (Imageomics Institute) - ORCID: 0000-0003-0846-2413
* Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629
* Jackson Miliko (Mpala Research Centre)
* Eduardo Bessa (Universidade de Brasília) - ORCID: 0000-0003-0606-5860
* Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412
* Daniel Rubenstein (Princeton University) - ORCID: 0000-0001-9049-5219
* Charles Stewart (Rensselaer Polytechnic Institute)