# Florida Wildlife Camera Trap Dataset

arXiv: https://arxiv.org/abs/2106.12628 · HTML: https://mlsanity.com/paper/2106.12628/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018)
- [Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks](https://mlsanity.com/paper/1603.06169/) (2016)

## Built on by (most cited first, 7 of 7)

- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture](https://mlsanity.com/paper/2303.13174/) (2023)
- [Ensembles of Vision Transformers as a New Paradigm for Automated Classification in Ecology](https://mlsanity.com/paper/2203.01726/) (2022)
- [CVB: A Video Dataset of Cattle Visual Behaviors](https://mlsanity.com/paper/2305.16555/) (2023)
- [OpenAnimalTracks: A Dataset for Animal Track Recognition](https://mlsanity.com/paper/2406.09647/) (2024)
- [Meerkat Behaviour Recognition Dataset](https://mlsanity.com/paper/2306.11326/) (2023)
- [A Review on Coarse to Fine-Grained Animal Action Recognition](https://mlsanity.com/paper/2506.01214/) (2025)

## Abstract (arXiv)

Trail camera imagery has increasingly gained popularity amongst biologists for conservation and ecological research. Minimal human interference required to operate camera traps allows capturing unbiased species activities. Several studies - based on human and wildlife interactions, migratory patterns of various species, risk of extinction in endangered populations - are limited by the lack of rich data and the time-consuming nature of manually annotating trail camera imagery. We introduce a challenging wildlife camera trap classification dataset collected from two different locations in Southwestern Florida, consisting of 104,495 images featuring visually similar species, varying illumination conditions, skewed class distribution, and including samples of endangered species, i.e. Florida panthers. Experimental evaluations with ResNet-50 architecture indicate that this image classification-based dataset can further push the advancements in wildlife statistical modeling. We will make the dataset publicly available.