# Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1603.06169 · HTML: https://mlsanity.com/paper/1603.06169/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2016.

## Built on by (most cited first, 28 of 28)

- [The iWildCam 2021 Competition Dataset](https://mlsanity.com/paper/2105.03494/) (2021)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [Adapting to Online Label Shift with Provable Guarantees](https://mlsanity.com/paper/2207.02121/) (2022)
- [P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications](https://mlsanity.com/paper/2203.04737/) (2022)
- [Automated wildlife image classification: An active learning tool for ecological applications](https://mlsanity.com/paper/2303.15823/) (2023)
- [Filtering Empty Camera Trap Images in Embedded Systems](https://mlsanity.com/paper/2104.08859/) (2021)
- [Florida Wildlife Camera Trap Dataset](https://mlsanity.com/paper/2106.12628/) (2021)
- [Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence](https://mlsanity.com/paper/2202.02283/) (2022)
- [CATALOG: A Camera Trap Language-guided Contrastive Learning Model](https://mlsanity.com/paper/2412.10624/) (2024)
- [Bag of Tricks for Long-Tail Visual Recognition of Animal Species in Camera-Trap Images](https://mlsanity.com/paper/2206.12458/) (2022)
- [Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps](https://mlsanity.com/paper/2304.12703/) (2023)
- [Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data](https://mlsanity.com/paper/2411.14219/) (2024)
- [Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset](https://mlsanity.com/paper/2408.00002/) (2024)
- [SPOTS-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms](https://mlsanity.com/paper/2410.21044/) (2024)
- [Improving Wildlife Out-of-Distribution Detection: Africas Big Five](https://mlsanity.com/paper/2506.06719/) (2025)
- [Two-phase training mitigates class imbalance for camera trap image classification with CNNs](https://mlsanity.com/paper/2112.14491/) (2021)
- [Elephants and Algorithms: A Review of the Current and Future Role of AI in Elephant Monitoring](https://mlsanity.com/paper/2306.13803/) (2023)
- [Metadata augmented deep neural networks for wild animal classification](https://mlsanity.com/paper/2409.04825/) (2024)
- [Nearest-Class Mean and Logits Agreement for Wildlife Open-Set Recognition](https://mlsanity.com/paper/2510.17338/) (2025)
- [WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift](https://mlsanity.com/paper/2601.00993/) (2026)
- [Wild Animal Classifier Using CNN](https://mlsanity.com/paper/2210.07973/) (2022)
- [A Learned Simulation Environment to Model Plant Growth in Indoor Farming](https://mlsanity.com/paper/2212.03155/) (2022)
- [Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring](https://mlsanity.com/paper/2412.16329/) (2024)
- [Frequency-Adaptive Discrete Cosine-ViT-ResNet Architecture for Sparse-Data Vision](https://mlsanity.com/paper/2505.22701/) (2025)
- [Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models](https://mlsanity.com/paper/2508.15629/) (2025)
- [Exploring Clustering Capability of Inpainting Model Embeddings for Pattern-based Individual Identification](https://mlsanity.com/paper/2605.04904/) (2026)
- [Multi-Object Tracking Consistently Improves Wildlife Inference](https://mlsanity.com/paper/2605.16672/) (2026)
- [Camera trap classification with deep learning under ground truth uncertainty](https://mlsanity.com/paper/2608.30789/) (2026)

## Abstract (arXiv)

Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high volume of data (in the order on thousands or millions of images), which must be analyzed by a human expert. In this work, a method for animal species identification in the wild using very deep convolutional neural networks is presented. Multiple versions of the Snapshot Serengeti dataset were used in order to probe the ability of the method to cope with different challenges that camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and 98.1% in Top-5 in the evaluation set using a residual network topology. Also, the results show that the proposed method outperforms previous approximations and proves that recognition in camera-trap images can be automated.