# Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data

arXiv: https://arxiv.org/abs/2304.09657 · HTML: https://mlsanity.com/paper/2304.09657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 63 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018)
- [Wildbook: Crowdsourcing, computer vision, and data science for conservation](https://mlsanity.com/paper/1710.08880/) (2017)
- [Deep CNN-based Multi-task Learning for Open-Set Recognition](https://mlsanity.com/paper/1903.03161/) (2019)
- [Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales](https://mlsanity.com/paper/1708.07785/) (2017)
- [Flexible and Scalable Deep Learning with MMLSpark](https://mlsanity.com/paper/1804.04031/) (2018)

## Built on by (most cited first, 2 of 2)

- [Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data](https://mlsanity.com/paper/2406.13579/) (2024)
- [CNN Based Flank Predictor for Quadruped Animal Species](https://mlsanity.com/paper/2406.13588/) (2024)

## Abstract (arXiv)

The manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals. In this study, we developed a pipeline to automatically analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus). We assumed that the same individual was seen throughout one triggered video sequence. With this assumption, multiple images could be assigned to an individual for the initial database filling without pre-labeling. The pipeline was based on well-established components from computer vision and deep learning, particularly convolutional neural networks (CNNs) and scale-invariant feature transform (SIFT) features. We augmented this basis by implementing additional components to substitute otherwise required human interactions. Based on the similarity between frames from the video material, clusters were formed that represented individuals bypassing the open set problem of the unknown total population. The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a success rate of over 83% for correct matches between previously unknown individuals. The proposed pipeline can become a valuable tool for future conservation projects based on camera trap data, reducing the work of manual analysis for individual identification, when labeled data is unavailable.