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
Real-Time High-Resolution Background Matting

Real-Time High-Resolution Background Matting Official repository for the paper Real-Time High-Resolution Background Matting. Our model requires captur

Peter Lin 6.1k Jan 03, 2023
Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors

SSL_OSC Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors

zaixizhang 2 May 14, 2022
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021)

OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021) This is an PyTorch implementation of OpenMatc

Vision and Learning Group 38 Dec 26, 2022
Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"

Hold me tight! Influence of discriminative features on deep network boundaries This is the source code to reproduce the experiments of the NeurIPS 202

EPFL LTS4 19 Dec 10, 2021
Official implementation of YOGO for Point-Cloud Processing

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module By Chenfeng Xu, Bohan Zhai, Bichen Wu, T

Chenfeng Xu 67 Dec 20, 2022
Pyeventbus: a publish/subscribe event bus

pyeventbus pyeventbus is a publish/subscribe event bus for Python 2.7. simplifies the communication between python classes decouples event senders and

15 Apr 21, 2022
A deep neural networks for images using CNN algorithm.

Example-CNN-Project This is a simple project showing how to implement deep neural networks using CNN algorithm. The dataset is taken from this link: h

Mohammad Amin Dadgar 3 Sep 16, 2022
load .txt to train YOLOX, same as Yolo others

YOLOX train your data you need generate data.txt like follow format (per line- one image). prepare one data.txt like this: img_path1 x1,y1,x2,y2,clas

LiMingf 18 Aug 18, 2022
We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Multi-Modal Self-Supervision using GDT and StiCa This is an official pytorch implementation of papers: Multi-modal Self-Supervision from Generalized D

Facebook Research 42 Dec 09, 2022
Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)

Graph-to-Tree Learning for Solving Math Word Problems PyTorch implementation of Graph based Math Word Problem solver described in our ACL 2020 paper G

Jipeng Zhang 66 Nov 23, 2022
Cross-modal Deep Face Normals with Deactivable Skip Connections

Cross-modal Deep Face Normals with Deactivable Skip Connections Victoria Fernández Abrevaya*, Adnane Boukhayma*, Philip H. S. Torr, Edmond Boyer (*Equ

72 Nov 27, 2022
A particular navigation route using satellite feed and can help in toll operations & traffic managemen

How about adding some info that can quanitfy the stress on a particular navigation route using satellite feed and can help in toll operations & traffic management The current analysis is on the satel

Ashish Pandey 1 Feb 14, 2022
PN-Net a neural field-based framework for depth estimation from single-view RGB images.

PN-Net We present a neural field-based framework for depth estimation from single-view RGB images. Rather than representing a 2D depth map as a single

1 Oct 02, 2021
Repository for RNNs using TensorFlow and Keras - LSTM and GRU Implementation from Scratch - Simple Classification and Regression Problem using RNNs

RNN 01- RNN_Classification Simple RNN training for classification task of 3 signal: Sine, Square, Triangle. 02- RNN_Regression Simple RNN training for

Nahid Ebrahimian 13 Dec 13, 2022
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning

Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning Yansong Tang *, Zhenyu Jiang *, Zhenda Xie *, Yue

Zhenyu Jiang 12 Nov 16, 2022
Winning solution of the Indoor Location & Navigation Kaggle competition

This repository contains the code to generate the winning solution of the Kaggle competition on indoor location and navigation organized by Microsoft

Tom Van de Wiele 62 Dec 28, 2022
Adaptive Graph Convolution for Point Cloud Analysis

Adaptive Graph Convolution for Point Cloud Analysis This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph

64 Dec 21, 2022
Asymmetric Bilateral Motion Estimation for Video Frame Interpolation, ICCV2021

ABME (ICCV2021) Junheum Park, Chul Lee, and Chang-Su Kim Official PyTorch Code for "Asymmetric Bilateral Motion Estimation for Video Frame Interpolati

Junheum Park 86 Dec 28, 2022
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback This is our Pytorch implementation for the paper: Yinwei Wei,

17 Jun 10, 2022
DumpSMBShare - A script to dump files and folders remotely from a Windows SMB share

DumpSMBShare A script to dump files and folders remotely from a Windows SMB shar

Podalirius 178 Jan 06, 2023