AlphaPose-master
所属分类：模式识别(视觉/语音等)
开发工具：Python
文件大小：35118KB
下载次数：24
上传日期：2019-03-31 09:38:14
上 传 者：
对对对对的
说明： alpha pose是一个精确的多人姿态估计量，它是第一个实时开放源代码系统，在coco数据集上实现了70+map（72.3 map），在mpii数据集上实现了80+map（82.1 map）。*为了匹配帧间与同一个人相对应的姿态，我们还提供了一个高效的在线姿态跟踪器，称为姿态流。它是第一个开源的在线姿势跟踪器，在posetrack挑战数据集上实现了60+地图（66.5地图）和50+mota（58.3 mota）。
(Alpha Pose is an accurate multi-person pose estimator, which is the first real-time open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset.** To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.)
文件列表:
LICENSE (34706, 2019-03-15)
PoseFlow (0, 2019-03-15)
PoseFlow\alpha-pose-results-sample.json (5108, 2019-03-15)
PoseFlow\deepmatching (0, 2019-03-15)
PoseFlow\deepmatching\COPYING (35147, 2019-03-15)
PoseFlow\deepmatching\Makefile (1673, 2019-03-15)
PoseFlow\deepmatching\array_types.h (7659, 2019-03-15)
PoseFlow\deepmatching\climb1.png (198296, 2019-03-15)
PoseFlow\deepmatching\climb2.png (168518, 2019-03-15)
PoseFlow\deepmatching\conv.cpp (32320, 2019-03-15)
PoseFlow\deepmatching\conv.h (4611, 2019-03-15)
PoseFlow\deepmatching\conv.o (400112, 2019-03-15)
PoseFlow\deepmatching\deep_matching.cpp (32011, 2019-03-15)
PoseFlow\deepmatching\deep_matching.h (4375, 2019-03-15)
PoseFlow\deepmatching\deep_matching.o (251816, 2019-03-15)
PoseFlow\deepmatching\deepmatching (648692, 2019-03-15)
PoseFlow\deepmatching\deepmatching-static (3283502, 2019-03-15)
PoseFlow\deepmatching\deepmatching.i (6554, 2019-03-15)
PoseFlow\deepmatching\deepmatching.m (1149, 2019-03-15)
PoseFlow\deepmatching\deepmatching.py (3976, 2019-03-15)
PoseFlow\deepmatching\deepmatching_matlab.cpp (5199, 2019-03-15)
PoseFlow\deepmatching\deepmatching_wrap.c (117871, 2019-03-15)
PoseFlow\deepmatching\dino1.jpg (79949, 2019-03-15)
PoseFlow\deepmatching\dino2.jpg (29465, 2019-03-15)
PoseFlow\deepmatching\hog.cpp (24554, 2019-03-15)
PoseFlow\deepmatching\hog.h (2421, 2019-03-15)
PoseFlow\deepmatching\hog.o (289880, 2019-03-15)
PoseFlow\deepmatching\image.cpp (6570, 2019-03-15)
PoseFlow\deepmatching\image.h (2448, 2019-03-15)
PoseFlow\deepmatching\image.o (44184, 2019-03-15)
PoseFlow\deepmatching\io.cpp (10428, 2019-03-15)
PoseFlow\deepmatching\io.h (1047, 2019-03-15)
PoseFlow\deepmatching\io.o (88552, 2019-03-15)
PoseFlow\deepmatching\liberty1.png (3959, 2019-03-15)
PoseFlow\deepmatching\liberty2.png (3777, 2019-03-15)
PoseFlow\deepmatching\main.cpp (12495, 2019-03-15)
... ...
## AlphaPose
[Alpha Pose](http://www.mvig.org/research/alphapose.html) is an accurate multi-person pose estimator, which is the **first real-time** open-source system that achieves **70+ mAP (72.3 mAP)** on COCO dataset and **80+ mAP (82.1 mAP)** on MPII dataset.**
To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the **first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.**
## News!
- Feb 2019: [CrowdPose](https://github.com/MVIG-SJTU/AlphaPose/blob/pytorch/doc/CrowdPose.md) is integrated into AlphaPose Now!
- Dec 2018: [General version](https://github.com/MVIG-SJTU/AlphaPose/tree/master/PoseFlow) of PoseFlow is released! 3X Faster and support pose tracking results visualization!
- Sep 2018: [**PyTorch** version](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch) of AlphaPose is released! It runs at **20 fps** on COCO validation set (4.6 people per image on average) and achieves 71 mAP!
## Contents
- [AlphaPose](#alphapose)
- [News!](#news)
- [Contents](#contents)
- [Results](#results)
- [Pose Estimation](#pose-estimation)
- [Pose Tracking](#pose-tracking)
- [CrowdPose](#crowdpose)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Output](#output)
- [Speeding Up AlphaPose](#speeding-up-alphapose)
- [Feedbacks](#feedbacks)
- [Contributors](#contributors)
- [Citation](#citation)
- [License](#license)
## Results
### Pose Estimation
Results on COCO test-dev 2015:
| Method | AP @0.5:0.95 | AP @0.5 | AP @0.75 | AP medium | AP large |
|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|
| OpenPose (CMU-Pose) | 61.8 | 84.9 | 67.5 | 57.1 | 68.2 |
| Detectron (Mask R-CNN) | 67.0 | 88.0 | 73.1 | 62.2 | 75.6 |
| **AlphaPose** | **72.3** | **89.2** | **79.1** | **69.0** | **78.6** |
Results on MPII full test set:
| Method | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Ave |
|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| OpenPose (CMU-Pose) | 91.2 | 87.6 | 77.7 | 66.8 | 75.4 | 68.9 | 61.7 | 75.6 |
| Newell & Deng | **92.1** | 89.3 | 78.9 | 69.8 | 76.2 | 71.6 | ***.7 | 77.5 |
| **AlphaPose** | 91.3 | **90.5** | **84.0** | **7***** | **80.3** | **79.9** | **72.4** | **82.1** |
### Pose Tracking
Results on PoseTrack Challenge validation set:
1. Task2: Multi-Person Pose Estimation (mAP)
| Method | Head mAP | Shoulder mAP | Elbow mAP | Wrist mAP | Hip mAP | Knee mAP | Ankle mAP | Total mAP |
|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| Detect-and-Track(FAIR) | **67.5** | 70.2 | 62 | 51.7 | 60.7 | 58.7 | 49.8 | 60.6 |
| **AlphaPose** | 66.7 | **73.3** | **68.3** | **61.1** | **67.5** | **67.0** | **61.3** | **66.5** |
2. Task3: Pose Tracking (MOTA)
| Method | Head MOTA | Shoulder MOTA | Elbow MOTA | Wrist MOTA | Hip MOTA | Knee MOTA | Ankle MOTA | Total MOTA | Total MOTP| Speed(FPS) |
|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| Detect-and-Track(FAIR) | **61.7** | 65.5 | 57.3 | 45.7 | 54.3 | 53.1 | 45.7 | 55.2 | 61.5 |Unknown|
| **PoseFlow(DeepMatch)** | 59.8 | **67.0** | 59.8 | 51.6 | **60.0** | **58.4** | **50.5** | **58.3** | **67.8**|8|
| **PoseFlow(OrbMatch)** | 59.0 | 66.8 | **60.0** | **51.8** | 59.4 | **58.4** | 50.3 | 58.0 | 62.2|24|
*Note: Please read [PoseFlow/README.md](PoseFlow/) for details.*
### CrowdPose
**Results on CrowdPose Validation:**
*Compare with state-of-the-art methods*
| Method | AP @0.5:0.95 | AP @0.5 | AP @0.75 | AR @0.5:0.95 | AR @0.5 | AR @0.75 |
|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|:-------:|
| Detectron (Mask R-CNN) | 57.2 | 83.5 | 60.3 | 65.9 | 89.3 | 69.4 |
| Simple Pose (Xiao *et al.*) | 60.8 | 81.4 | 65.7 | 67.3 | 86.3 | 71.8 |
| **Ours** | **66.0** | **84.2** | **71.5** | **72.7** | **89.5** | **77.5** |
*Compare with open-source systems*
| Method | AP @*Easy* | AP @*Medium* | AP @*Hard* | FPS |
|:-------|:-----:|:-------:|:-------:|:-------:|
| OpenPose (CMU-Pose) | 62.7 | 48.7 | 32.3 | 5.3 |
| Detectron (Mask R-CNN) | 69.4 | 57.9 | 45.8 | 2.9 |
| **Ours** | **75.5** | **66.3** | **57.4** | **10.1** |
*Note: Please read [doc/CrowdPose.md](https://github.com/MVIG-SJTU/AlphaPose/blob/pytorch/doc/CrowdPose.md) for details.*
## Installation
***Note: For new users or users that are not familiar with TensorFlow or Torch, we suggest using the [**PyTorch** version](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch) since it's more user-friendly and runs faster.***
1. Get the code and build related modules.
```Shell
git clone https://github.com/MVIG-SJTU/AlphaPose.git
cd AlphaPose/human-detection/lib/
make clean
make
cd newnms/
make
cd ../../../
```
2. Install [Torch](https://github.com/torch/distro) and [TensorFlow](https://www.tensorflow.org/install/)(verson >= 1.2). After that, install related dependencies by:
```Shell
chmod +x install.sh
./install.sh
```
3. Run fetch_models.sh to download our pre-trained models. Or download the models manually: output.zip([Google drive](https://drive.google.com/open?id=1dMiUPMvt5o-S1BjDkzUJooEoT3GgasxB)|[Baidu pan](https://pan.baidu.com/s/1hund0US)), final_model.t7([Google drive](https://drive.google.com/open?id=1JYlLspGJHJFIggkDll4MdUdqX2ELqHpk)|[Baidu pan](https://pan.baidu.com/s/1qZuEyF6))
```Shell
chmod +x fetch_models.sh
./fetch_models.sh
```
## Quick Start
- **Demo**: Run AlphaPose for all images in a folder and visualize the results with:
```
./run.sh --indir examples/demo/ --outdir examples/results/ --vis
```
The visualized results will be stored in examples/results/RENDER. To easily process images/video and display/save the results, please see [doc/run.md](doc/run.md). **If you get any problems, you can check the [doc/faq.md](doc/faq.md).**
- **Video**: You can see our video demo [here](https://www.youtube.com/watch?v=Z2WPd59pRi8).
## Output
Output (format, keypoint index ordering, etc.) in [doc/output.md](doc/output.md).
## Speeding Up AlphaPose
We provide a `fast` mode for human-detection that disables multi-scale testing. You can turn it on by adding `--mode fast`.
And if you have multiple gpus on your machine or have large gpu memories, you can speed up the pose estimation step by using multi-gpu testing or large batch tesing with:
```
./run.sh --indir examples/demo/ --outdir examples/results/ --gpu 0,1,2,3 --batch 5
```
It assumes that you have 4 gpu cards on your machine and *each card* can run a batch of 5 images. Here is the recommended batch size for gpu with different size of memory:
```
GPU memory: 4GB -- batch size: 3
GPU memory: 8GB -- batch size: 6
GPU memory: 12GB -- batch size: 9
```
See [doc/run.md](doc/run.md) for more details.
## Feedbacks
If you get any problems, you can check the [doc/faq.md](doc/faq.md) first. If it can not solve your problems or if you find any bugs, don't hesitate to comment on GitHub or make a pull request!
## Contributors
AlphaPose is based on RMPE(ICCV'17), authored by [Hao-shu Fang](https://fang-haoshu.github.io/), Shuqin Xie, [Yu-Wing Tai](https://scholar.google.com/citations?user=nFhLmFkAAAAJ&hl=en) and [Cewu Lu](http://www.mvig.org/), [Cewu Lu](http://mvig.sjtu.edu.cn/) is the corresponding author. Currently, it is developed and maintained by [Hao-shu Fang](https://fang-haoshu.github.io/), [Jiefeng Li](http://jeff-leaf.site/), [Yuliang Xiu](http://xiuyuliang.cn/about/) and [Ruiheng Chang](https://crh19970307.github.io/).
The main contributors are listed in [doc/contributors.md](doc/contributors.md).
## Citation
Please cite these papers in your publications if it helps your research:
@inproceedings{fang2017rmpe,
title={{RMPE}: Regional Multi-person Pose Estimation},
author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},
booktitle={ICCV},
year={2017}
}
@inproceedings{xiu2018poseflow,
title = {{Pose Flow}: Efficient Online Pose Tracking},
author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
booktitle={BMVC},
year = {2018}
}
## License
AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please drop an e-mail at mvig.alphapose[at]gmail[dot]com and cc lucewu[[at]sjtu[dot]edu[dot]cn. We will send the detail agreement to you.
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