Spiideo SoccerNet SynLoc

Single Frame World Coordinate Athlete Detection and Localization with Synthetic Data

[Paper] [Baseline] [Devkit]

Spiideo SoccerNet SynLoc - Single Frame World Coordinate Athlete Detection and Localization with Synthetic Data

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arxiv

Welcome to the Development Kit for the Spiideo SoccerNet SynLoc task and Challange. This kit is meant as a help to get started working with the data and the proposed task.

Installation

The easiest way to install sskit is to use pip:

  pip install sskit

It is also possible to build manually:

  git clone https://github.com/Spiideo/sskit.git
  cd sskit
  python setup.py install

mAP-LocSim Evaluation

Tools for evaluating a solution using the proposed mAP-LocSim metrics can be found in sskit.coco. It's an adaption of xtcocotools, and is used in a similar way. Annotations and results are stored in coco format with the ground location of the objects placed in the position_on_pitch key as a 2D pitch coordinate in meters. For convenience it is also possible to place the detected location in image space as one of the keypoints. That image location will then be projected onto the ground plane using the camera model. To do that set the position_from_keypoint_index parameter to the index of the keypoint containing the image location as indicated by the code line commented out. To evaluate results on the validation set stored in validation_results.json, use:

  from xtcocotools.coco import COCO
  from sskit.coco import LocSimCOCOeval

  coco = COCO('data/annotations/val.json')
  coco_det = coco.loadRes("validation_results.json")
  coco_eval = LocSimCOCOeval(coco, coco_det, 'bbox', [0.089, 0.089], True)
  coco_eval.params.useSegm = None
  # coco_eval.params.position_from_keypoint_index = 1

  coco_eval.evaluate()
  coco_eval.accumulate()
  coco_eval.summarize()

  score_threshold = coco_eval.stats[15]

This will select a score threshold that maximizes the F1-score, which will make that score biased for the validation set. To get unbiased scores on the test-set, the score threshold found for the validation set should be used there:

  from xtcocotools.coco import COCO
  from sskit.coco import LocSimCOCOeval

  coco = COCO('data/annotations/test.json')
  coco_det = coco.loadRes("test_results.json")
  coco_eval = LocSimCOCOeval(coco, coco_det, 'bbox', [0.089, 0.089], True)
  coco_eval.params.useSegm = None
  # coco_eval.params.position_from_keypoint_index = 1
  coco_eval.params.score_threshold = score_threshold

  coco_eval.evaluate()
  coco_eval.accumulate()
  coco_eval.summarize()

Camera model

The camera model used in the dataset is a standard projective pihole camera model with radial distortion. There are several different coordinate systems used (se pictures below), and functions to convert points between them can be found in sskit. The World coordinates are either 3D or 2D ground coordinates with the last coordinate assumed to be 0. The graph below shows the different coordinate systems and how they relate to eachother:

  graph LR;
  Camera -- normalize() --> Normalized
  Normalized -- unnormalize() --> Camera
  Normalized -- undistort() --> Undistorted
  Undistorted -- distort() --> Normalized
  Undistorted -- undistorted_to_ground() --> World
  World -- world_to_undistorted() --> Undistorted
  World -- world_to_image() --> Normalized
  Normalized -- image_to_ground() --> World

Loading

To for eaxample project the center of the pitch, world coordinate (0,0,0), into the pytorch image, img, using a camera matrix, camera_matrix and distortion parameters, dist_poly, from the dataset, use:

  from sskit import unnormalize, world_to_image

  unnormalize(world_to_image(camera_matrix, dist_poly, [0.0, 0.0, 0.0]), img.shape)

A more comprehensive example that was used to create the illustrations below can be found in coordinate_systems.py.

Camera Image

Normalized Image

Undistorted Image

Ground Plane

Citation

If you use this code for your research, please cite: