Gorilla-SPAC-Wild: Large-Scale Video Dataset for Gorilla Re-Identification

Overview

Gorilla-SPAC-Wild is a comprehensive benchmark dataset for individual re-identification of Western Lowland Gorillas from camera trap footage in natural rainforest environments. This dataset addresses a critical bottleneck in conservation: automating the analysis of vast archives of camera trap video to track endangered gorilla populations non-invasively.

This dataset is part of the GorillaWatch project, which introduces an end-to-end pipeline integrating detection, tracking, and re-identification for automated gorilla monitoring.

Dataset Description

Key Features

• Large-Scale Video Dataset: Extracted from camera trap footage at Odzala-Kokoua National Park, Republic of Congo
• 108 Individual Gorillas: Tracked across multiple encounters in natural, challenging lighting and environmental conditions
• Paired Face-Body Crops: Each gorilla is annotated with both high-quality facial crops (≥50×50 pixels) and full-body crops, enabling comprehensive re-identification analysis
• Cross-Encounter Data: Gorillas recorded in different sessions and locations, simulating real-world re-identification challenges where appearance changes due to varying environmental conditions
• Open-Set Evaluation: Strict individual-based split suitable for real-world scenarios where new individuals frequently appear

Data Extraction & Processing

• Images are extracted from automatically generated tracklets using YOLOv8-Nano and BoostTrack++
• Ground truth labels provided by primate researchers with 15+ years of field experience with this population
• Face detections filtered to ensure quality (≥50×50 pixels minimum)

Dataset Splits

• Train (70%): For training re-identification models
• Validation (15%): For hyperparameter tuning and model selection
• Test (15%): For final evaluation on unseen encounters
• Single-Encounter: All gorillas with only single encounter

Dataset Schema

Each sample includes:

• image: Face crop of the gorilla (encoded as image bytes)
• class: Individual gorilla identifier (anonymized ID)
• date: Capture date (YYYYMMDD format)
• video: Source video identifier
• frame_number: Frame number within the video
• camera: Camera trap identifier
• file_name: Path to the face image file

License

This dataset is released under the CC-BY-4.0 License.

Use Cases

Primary Applications

1. Individual Re-Identification: Training models to identify individual gorillas from camera trap footage
2. Conservation Monitoring: Automating population tracking for demographic studies
3. Disease Tracking: Monitoring health and injury patterns in wildlife populations
4. Behavioral Analysis: Long-term observation of social dynamics

Dataset Statistics

• Total Samples: Thousands of annotated gorilla face/body image pairs
• Training Samples: ~70% of dataset
• Validation Samples: ~15% of dataset
• Test Samples: ~15% of dataset
• Temporal Span: Multiple years of camera trap recordings
• Locations: Odzala-Kokoua National Park, Republic of Congo

Loading and Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("gorilla-watch/Gorilla-SPAC-Wild", "face")

# Split structure
train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test"]

# Access samples
sample = train_data[0]
face_image = sample["image"]  # PIL Image object
gorilla_id = sample["class"]   # Individual identifier

Ethical Considerations

• This dataset is used exclusively for non-invasive wildlife conservation and monitoring
• All data collection followed ethical guidelines for field research
• The dataset supports critical conservation efforts for an endangered species

Citation

If you use this dataset in your research, please cite:

@inproceedings{GorillaWatch2026,
  title={GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring}, 
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  author={Maximilian Schall and Felix Leonard Knöfel and Noah Elias König and Jan Jonas Kubeler and Maximilian von Klinski and Joan Wilhelm Linnemann and Xiaoshi Liu and Iven Jelle Schlegelmilch and Ole Woyciniuk and Alexandra Schild and Dante Wasmuht and Magdalena Bermejo Espinet and German Illera Basas and Gerard de Melo},
  year={2026},
  archivePrefix={arXiv},
  eprint={2512.07776}
}

Acknowledgments

The project on which this report is based was funded by the Federal Ministry of Research, Technology and Space under the funding code “KI-Servicezentrum Berlin-Brandenburg” 16IS22092. We acknowledge the support of Sabine Plattner African Charities (SPAC) for their funding to this research. We are grateful to Zoo Berlin for their expert assistance and facility access. This collaboration enabled the development of AI tools capable of being deployed in the wild to directly support gorilla conservation. The responsibility for the content of this publication remains with the authors.