README.md

---
license: apache-2.0
language:
- en
tags:
- computer-vision
- animal-pose-estimation
- multi-object-tracking
- occlusion-handling
- image-segmentation
- inpainting
- deep-learning
- video-analysis
arxiv: 2512.07712
---



# UnCageNet

**UnCageNet** is a computer vision framework for robust **animal tracking and pose estimation in caged environments**, where occlusions caused by cage bars significantly degrade the performance of existing methods.

This repository provides the **official implementation** of the paper:

> **UnCageNet: Tracking and Pose Estimation of Caged Animal**  
> Sayak Dutta, Harish Katti, Shashikant Verma, Shanmuganathan Raman  
> arXiv: https://arxiv.org/abs/2512.07712

🔗 **Code:** https://github.com/itz-sayak/UnCageNet

---

## 🔍 Method Overview

UnCageNet introduces a **three-stage preprocessing pipeline** that improves downstream tracking and pose estimation under structured occlusions:

1. **Cage Segmentation**  
   - Gabor-enhanced ResNet-UNet  
   - Orientation-aware filters (72 directional kernels)  
   - Accurate detection of cage bar structures

2. **Cage Inpainting**  
   - Content-aware reconstruction using **CRFill**  
   - Removes structured occlusions while preserving animal appearance

3. **Downstream Evaluation**  
   - Standard pose estimation and tracking models (e.g., STEP, ViTPose)  
   - Applied on “uncaged” frames for fair performance comparison

This pipeline enables performance **comparable to uncaged environments**, despite heavy occlusions.

---

## 📊 Experimental Highlights

- Significant improvement in:
  - Keypoint detection accuracy
  - Trajectory consistency
- Robust performance across:
  - Severe occlusion patterns
  - Long video sequences
- Plug-and-play compatibility with existing tracking and pose models

(Refer to the paper for full quantitative results.)

---

## 💡 Intended Use

UnCageNet is intended for:
- Animal behavior analysis
- Zoological and veterinary monitoring
- Laboratory animal studies
- Long-term tracking in constrained environments

---

## ⚠️ Limitations

- Assumes **structured occlusions** (e.g., cage bars)
- Performance may degrade for:
  - Highly deformable or unstructured occluders
  - Extremely low-resolution video
- Not trained for arbitrary object categories beyond animals

---

## 📄 Citation

If you use this work, please cite:

```bibtex
@article{dutta2025uncagenet,
  title   = {UnCageNet: Tracking and Pose Estimation of Caged Animal},
  author  = {Dutta, Sayak and Katti, Harish and Verma, Shashikant and Raman, Shanmuganathan},
  journal = {arXiv preprint arXiv:2512.07712},
  year    = {2025}
}