Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

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Deep Learning Part-Aware_Measurement_for_3D_Pose_Estimation_and_Tracking
Overview

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking
Hau Chu, Jia-Hong Lee, Yao-Chih Lee, Ching-Hsien Hsu, Jia-Da Li, Chu-Song Chen
2021 CVPR B-AMFG Workshop

Note: It's a project of AI^2 Lab. The code will be update in here while there is a new version.

Installation

  • Python 3.6+

  • Cuda 9.0

  • Cudnn 7

  • gcc 5 & g++ 5 (for Ubuntu 18.04)

$ sudo apt install gcc-5 g++-5
$ sudo ln -s /usr/bin/gcc-6 /usr/local/bin/gcc
$ sudo ln -s /usr/bin/g++-6 /usr/local/bin/g++
  • Conda Env
$ conda create -n venv python=3.6
$ conda activate venv
$ conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch
$ pip install tensorflow_gpu==1.9.0
$ pip install -r requirements.txt
  • Git
$ sudo apt install git

Data preparation

Download datasets:

  1. Campus (http://campar.in.tum.de/Chair/MultiHumanPose)
  2. Shelf (http://campar.in.tum.de/Chair/MultiHumanPose)
  3. CMU Panoptic (https://github.com/CMU-Perceptual-Computing-Lab/panoptic-toolbox)

Dataset's camera_parameter.pickle download

The directory tree should look like below:

${ROOT}
    |-- CatchImage
        |-- CampusSeq1
        |   |-- Camera0
        |   |-- Camera1
        |   |-- Camera2
        |   |-- camera_parameter.pickle
        |   |-- actorsGT.mat
        |-- Shelf
        |   |-- Camera0
        |   |-- ...
        |   |-- Camera4
        |   |-- camera_parameter.pickle
        |   |-- actorsGT.mat
        |-- Panoptic
        |   |-- 160906_pizza1
            |   |-- 00_03 # hdImgs folder of 03 camera
            |   |-- 00_06 # hdImgs folder of 06 camera
            |   |-- ...
            |   |-- camera_parameter.pickle
            |   |-- hdPose_stage1_coco19
            |-- ...
    |-- src

Backend Models

Backend models, which is not our works, are released codes from others. We only did some small modifications to fit the format of our input/output. Put models in {ROOT}/src/backend

  1. YOLOv3
  2. HRNet

Run Codes

Demo

$cd src
python -W ignore testmodel.py --dataset CampusSeq1 # For Campus
python -W ignore testmodel.py --dataset Shelf # For Shelf
python -W ignore testmodel.py --dataset Panoptic # For Panoptic (sub-dataset can be modified in config)

Evaluation

$cd src
python -W ignore evalmodel.py --dataset CampusSeq1 
python -W ignore evalmodel.py --dataset Shelf

Campus PCP Score

Bone Group Actor 0 Actor 1 Actor 2 Average
Head 100.00 100.00 100.00 100.00
Torso 100.00 100.00 100.00 100.00
Upper arms 98.98 100.00 100.00 99.66
Lower arms 92.86 68.78 91.30 84.31
Upper legs 100.00 100.00 100.00 100.00
Lower legs 100.00 100.00 100.00 100.00
Total 98.37 93.76 98.26 96.79

Shelf PCP Score

Bone Group Actor 0 Actor 1 Actor 2 Average
Head 94.98 100.00 91.30 95.43
Torso 100.00 100.00 100.00 100.00
Upper arms 100.00 100.00 96.27 98.76
Lower arms 98.21 77.03 96.27 90.50
Upper legs 100.00 100.00 100.00 100.00
Lower legs 100.00 100.00 100.00 100.00
Total 99.14 95.41 97.64 97.39

Citation

@InProceedings{Chu_2021_CVPR,
    author    = {Chu, Hau and Lee, Jia-Hong and Lee, Yao-Chih and Hsu, Ching-Hsien and Li, Jia-Da and Chen, Chu-Song},
    title     = {Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2021},
    pages     = {1472-1481}
}
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