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TensorFlow implementation of "Simple Baselines for Human Pose Estimation and Tracking", ECCV 2018
| Date | Stars |
|---|---|
| 2026-07-24 | 340 |
| 2026-07-25 | 339 |
| 2026-07-28 | 339 |
| 2026-07-30 | 339 |
| 2026-08-06 | 339 |
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# Simple Baselines for Human Pose Estimation and Tracking
<p align="center">
<img src="assets/1.jpg" width="400" height="250"> <img src="assets/2.jpg" width="400" height="250">
</p>
## Introduction
This repo is **[TensorFlow](https://www.tensorflow.org)** implementation of **[Simple Baselines for Human Pose Estimation and Tracking (ECCV 2018)](https://arxiv.org/abs/1804.06208)** of MSRA for **2D multi-person pose estimation** from a single RGB image.
**What this repo provides:**
* [TensorFlow](https://www.tensorflow.org) implementation of [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/abs/1804.06208).
* Flexible and simple code.
* Compatibility for most of the publicly available 2D multi-person pose estimation datasets including **[MPII](http://human-pose.mpi-inf.mpg.de/), [PoseTrack 2018](https://posetrack.net/), and [MS COCO 2017](http://cocodataset.org/#home)**.
* Human pose estimation visualization code (modified from [Detectron](https://github.com/facebookresearch/Detectron)).
## Dependencies
* [TensorFlow](https://www.tensorflow.org/)
* [CUDA](https://developer.nvidia.com/cuda-downloads)
* [cuDNN](https://developer.nvidia.com/cudnn)
* [Anaconda](https://www.anaconda.com/download/)
* [COCO API](https://github.com/cocodataset/cocoapi)
This code is tested under Ubuntu 16.04, CUDA 9.0, cuDNN 7.1 environment with two NVIDIA 1080Ti GPUs.
Python 3.6.5 version with Anaconda 3 is used for development.
## Directory
### Root
The `${POSE_ROOT}` is described as below.
```
${POSE_ROOT}
|-- data
|-- lib
|-- main
|-- tool
`-- output
```
* `data` contains data loading codes and soft links to images and annotations directories.
* `lib` contains kernel codes for 2d multi-person pose estimation system.
* `main` contains high-level codes for training or testing the network.
* `tool` contains dataset converter. I set MS COCO as reference format and provide mpii2coco and posetrack2coco converting code.
* `output` contains log, trained models, visualized outputs, and test result.
### Data
You need to follow directory structure of the `data` as below.
```
${POSE_ROOT}
|-- data
|-- |-- MPII
| `-- |-- dets
| | |-- human_detection.json
| |-- annotations
| | |-- train.json
| | `-- test.json
| `-- images
| |-- 000001163.jpg
| |-- 000003072.jpg
|-- |-- PoseTrack
| `-- |-- dets
| | |-- human_detection.json
| |-- annotations
| | |-- train2018.json
| | |-- val2018.json
| | `-- test2018.json
| |-- original_annotations
| | |-- train/
| | |-- val/
| | `-- test/
| `-- images
| |-- train/
| |-- val/
| `-- test/
|-- |-- COCO
| `-- |-- dets
| | |-- human_detection.json
| |-- annotations
| | |-- person_keypoints_train2017.json
| | |-- person_keypoints_val2017.json
| | `-- image_info_test-dev2017.json
| `-- images
| |-- train2017/
| |-- val2017/
| `-- test2017/
`-- |-- imagenet_weights
| |-- resnet_v1_50.ckpt
| |-- resnet_v1_101.ckpt
| `-- resnet_v1_152.ckpt
```
* In the `tool`, run `python mpii2coco.py` to convert MPII annotation files to MS COCO format (`MPII/annotations`).
* In the `tool`, run `python posetrack2coco.py` to convert PoseTrack annotation files to MS COCO format (`PoseTrack/annotations`).
* In the training stage, GT human bbox is used, and `human_detection.json` is used in testing stage which should be prepared before testing and follow [MS COCO format](http://cocodataset.org/#format-results).
* Download imagenet pre-trained resnet models from [tf-slim](https://github.com/tensorflow/models/tree/master/research/slim) and place it in the `data/imagenet_weights`.
* Except for `annotations` of the MPII and PoseTrack, all other directories are original version of downloaded ones.
* If you want to add your own dataset, you have to convert it to [MS COCO format](Excerpt of 10,055 characters
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matched fp:e68998c946cf5753, topic:deep-learning, topic:tensorflow
matched fp:e68998c946cf5753, topic:computer-vision, desc:pose estimation, readme:pose estimation