3D position tracking for soccer players with multi-camera videos

Overview

3D Player Tracking with Multi-View Stream

Project for 3DV 2021 Spring @ ETH Zurich [Report Link]


This repo contains a full pipeline to support 3D position tracking of soccer players, with multi-view calibrated moving/fixed video sequences as inputs.
- In single-camera tracking stage, Tracktor++ is used to get 2D positions.
- In multi-camera tracking stage, 2D positions are projected into 3D positions. Then across-camera association is achieved as an optimization problem with spatial, temporal and visual constraints.
- In the end, visualization in 2D, 3D and a voronoi visualization for sports coaching purpose are provided.
3D Tracking Sports Coaching
demo demo

Demo

Check demo scripts as examples

Currently, processed data is under protection due to legal issues.

  • Run the demo visualization on the moving cameras
bash script/demo_moving.sh
  • Run the demo visualization on the fixed cameras
bash script/demo_fix.sh

Preprocessing

  • Split video into image frames
python src/utils/v2img.py --pathIn=data/0125-0135/CAM1/CAM1.mp4 --pathOut=data/0125-0135/CAM1/img --splitnum=1
  • Estimate football pitch homography (size 120m * 90m ref)

FIFA official document

python src/utils/computeHomo.py --img=data/0125-0135/RIGHT/img/image0000.jpg --out_dir=data/0125-0135/RIGHT/
  • Handle moving cameras
python src/utils/mov2static.py --calib_file=data/calibration_results/0125-0135/CAM1/calib.txt --img_dir=data/0125-0135/CAM1/img --output_dir=data/0125-0135/CAM1/img_static
  • Convert ground truth/annotation json to text file
python src/utils/json2txt.py --jsonfile=data/0125-0135/0125-0135.json

Single-camera tracking

  • Object Detector: frcnn_fpn
    Train object detector and generate detection results with this Google Colab notebook. [pretrained model]
  • Run Tracktor++
    Put trainded object detector model_epoch_50.model into src/tracking_wo_bnw/output/faster_rcnn_fpn_training_soccer/.
    Put data and calibration results into src/tracking_wo_bnw/.
cd src/tracking_wo_bnw
python experiments/scripts/test_tracktor.py
  • Run ReID(team id) model
python src/team_classification/team_svm.py PATH_TO_TRACKING_RESULT PATH_TO_IMAGES
  • Convert tracking results to coordinates on the pitch

Equation to find the intersection of a line with a plane (ref)

python src/calib.py --calib_path=PATH_TO_CALIB --res_path=PATH_TO_TRACKING_RESULT --xymode --reid

# also plot the camera positions for fixed cameras
python src/calib.py --calib_path=PATH_TO_CALIB --res_path=PATH_TO_TRACKING_RESULT --viz

Across-camera association

  • Run two-cam tracker
python src/runMCTRacker.py 

# add team id constraint
python src/runMCTRacker.py --doreid
  • Run multi-cam tracker (e.g. 8 cams)
python src/runTreeMCTracker.py --doreid

Evaluation

  • Produce quatitative results (visualize results)

visualize 2d bounding box

# if format 
python src/utils/visualize.py --img_dir=data/0125-0135/RIGHT/img --result_file=output/tracktor/16m_right_prediction.txt 
# if format 
python src/utils/visualize.py --img_dir=data/0125-0135/RIGHT/img --result_file=output/iou/16m_right.txt --xymode
# if with team id
python src/utils/visualize.py --img_dir=data/0125-0135/RIGHT/img --result_file=output/tracktor/16m_right_prediction.txt --reid
# if 3d mode
python src/utils/visualize.py --img_dir=data/0125-0135/RIGHT/img --result_file=output/tracktor/RIGHT.txt --calib_file=data/calibration_results/0125-0135/RIGHT/calib.txt  --pitchmode

visualize 3d tracking result with ground truth and voronoi diagram

python src/utils/visualize_on_pitch.py --result_file=PATH_TO_TRACKING_RESULT --ground_truth=PATH_TO_GROUND_TRUTH

visualize 3d ground truth on camera frames (reprojection)

python src/utils/visualize_tracab --img_path=PATH_TO_IMAGES --calib_path=PATH_TO_CALIB --gt_path=PATH_TO_TRACAB_GT --output_path=PATH_TO_OUTPUT_VIDEO
  • Produce quantitative result
# 2d 
python src/motmetrics/apps/eval_motchallenge.py data/0125-0135/ output/tracktor_filtered

# 3d
python src/utils/eval3d.py --pred=output/pitch/EPTS_3_pitch.txt_EPTS_4_pitch.txt.txt --fixcam  --gt=data/fixedcam/gt_pitch_550.txt
python src/utils/eval3d.py --fixcam --boxplot

Acknowledgement

We would like to thank the following Github repos or softwares:

Authors

Yuchang Jiang, Tianyu Wu, Ying Jiao, Yelan Tao

Owner
Yuchang Jiang
Master student at ETH Zurich
Yuchang Jiang
Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM [News] The code for ACMH is released!!! [News] The code for ACMP is released!!! About ACMM is a multi-scale geometric consistency guided multi-vi

Qingshan Xu 118 Jan 04, 2023
A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

ICT.MIRACLE lab 75 Dec 26, 2022
A python script to dump all the challenges locally of a CTFd-based Capture the Flag.

A python script to dump all the challenges locally of a CTFd-based Capture the Flag. Features Connects and logins to a remote CTFd instance. Dumps all

Podalirius 77 Dec 07, 2022
Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F

Junjie Hu 13 Dec 10, 2022
SoGCN: Second-Order Graph Convolutional Networks

SoGCN: Second-Order Graph Convolutional Networks This is the authors' implementation of paper "SoGCN: Second-Order Graph Convolutional Networks" in Py

Yuehao 7 Aug 16, 2022
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

lbs-data Motivation Location data is collected from the public by private firms via mobile devices. Can this data also be used to serve the public goo

Alex 11 Sep 22, 2022
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

zhaohu xing 112 Dec 16, 2022
Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

Varun Nair 37 Dec 30, 2022
Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021

This folder contains the code for 'Scalable Variational Approaches for Bayesian Causal Discovery'. Installation To install, use conda with conda env c

14 Sep 21, 2022
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

haifeng xia 32 Oct 26, 2022
[CVPR'21] DeepSurfels: Learning Online Appearance Fusion

DeepSurfels: Learning Online Appearance Fusion Paper | Video | Project Page This is the official implementation of the CVPR 2021 submission DeepSurfel

Online Reconstruction 52 Nov 14, 2022
Activity tragle - Google is tracking everything, we just look at it

activity_tragle Google is tracking everything, we just look at it here. You need

BERNARD Guillaume 1 Feb 15, 2022
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Status: Under development (expect bug fixes and huge updates) ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectiv

37 Dec 28, 2022
A Python library that provides a simplified alternative to DBAPI 2

A Python library that provides a simplified alternative to DBAPI 2. It provides a facade in front of DBAPI 2 drivers.

Tony Locke 44 Nov 17, 2021
Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Parallel Tacotron2 Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Keon Lee 170 Dec 27, 2022
Compare neural networks by their feature similarity

PyTorch Model Compare A tiny package to compare two neural networks in PyTorch. There are many ways to compare two neural networks, but one robust and

Anand Krishnamoorthy 181 Jan 04, 2023
The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

DV Lab 71 Jan 06, 2023
Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Clay Mullis 82 Oct 13, 2022
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation, CVPR2022

TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation Paper Links: TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentati

Hust Visual Learning Team 253 Dec 21, 2022