[2023. 06. 28] This is still an old version. New version with pig tail tracking and beagle dog tracking will be updated soon. Substantial changes will be merged.
This repository contains the core code of MAMMAL system proposed in paper Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL (not publicly available yet).
This is the first system demonstrating pose and shape reconstruction of socially interacting animals. Moreover, it is the first system to analyse pig behavior in three-dimensional space.
The code is written using C++ Std 17 on Visual Studio Community 2017. Please refer to docs/INSTALL.md for installation guide. If you have any problems, raise an issue and I will comment as soon as possible.
Please refer to docs/Demo.md.
To enable the MAMMAL system, we proposed a new pig articulation model named PIG model, as in the link https://github.com/anl13/PIG_model. This link contains necessary details and code for you to understand the model.
Check out link https://github.com/anl13/MAMMAL_datasets for descriptions of these datasets. Briefly, BamaPig2D dataset contains 3340 images with more than 10k pig instances fully labeled with bounding boxes, silhouettes and keypoints, and is formated in COCO style. We also provide a modified COCO annotation file suitable for training SLEAP. BamaPig3D dataset provides synchronized 70-second videos of 10 views (1750 frames, 25FPS). We manually labeled 2D keypoints, 2D silhouettes and 3D mesh/keypoints every 25 frames, resulting in 70 fully labeled frames. BamaPig3D dataset is used for evaluating the performance of MAMMAL system. However, it is also suitable for training 3D algorithms (e.g. DANNCE or LiftPose3D). The figure shows examples of BamaPig2D dataset.
MAMMAL detection contains two seperate parts: pig bounding box and silhouette detection modified from Detectron2, and pig pose detection modified from HRNet. Please refer to https://github.com/anl13/pig_silhouette_det and https://github.com/anl13/pig_pose_det for more details.
Key evaluation code is released at https://github.com/anl13/MAMMAL_evaluation. It helps reproduce some figures of the paper. The results used for evaluation are also provided together with the code. Our provided results perfactly match the paper figures. However, if run the algorithm again, it may change a little (because we have updated the code recently), but not much.
Please refer to code https://github.com/anl13/MAMMAL_behavior for behavior analysis performed in the paper.
To effectively render the model in python environment, we developed a pig_renderer as in https://github.com/anl13/pig_renderer.
If you come up with any problem during testing MAMMAL, do not hesitate to propose an issue on github. I will reply when I see them.
If you use these datasets in your research, please cite the paper
@article{MAMMAL,
author = {An, Liang and Ren, Jilong and Yu, Tao and Hai, Tang and Jia, Yichang and Liu, Yebin},
title = {Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL},
journal = {unpublished},
year = {2023}
}