Camera Calibration and Player Localization in SoccerNet-v2 and
Investigation of their Representations for Action Spotting
- URL: http://arxiv.org/abs/2104.09333v1
- Date: Mon, 19 Apr 2021 14:21:05 GMT
- Title: Camera Calibration and Player Localization in SoccerNet-v2 and
Investigation of their Representations for Action Spotting
- Authors: Anthony Cioppa, Adrien Deli\`ege, Floriane Magera, Silvio Giancola,
Olivier Barnich, Bernard Ghanem, Marc Van Droogenbroeck
- Abstract summary: We distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset.
We leverage it to provide 3 ways of representing the calibration results along with player localization.
We exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2.
- Score: 61.92132798351982
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Soccer broadcast video understanding has been drawing a lot of attention in
recent years within data scientists and industrial companies. This is mainly
due to the lucrative potential unlocked by effective deep learning techniques
developed in the field of computer vision. In this work, we focus on the topic
of camera calibration and on its current limitations for the scientific
community. More precisely, we tackle the absence of a large-scale calibration
dataset and of a public calibration network trained on such a dataset.
Specifically, we distill a powerful commercial calibration tool in a recent
neural network architecture on the large-scale SoccerNet dataset, composed of
untrimmed broadcast videos of 500 soccer games. We further release our
distilled network, and leverage it to provide 3 ways of representing the
calibration results along with player localization. Finally, we exploit those
representations within the current best architecture for the action spotting
task of SoccerNet-v2, and achieve new state-of-the-art performances.
Related papers
- SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap [102.5232204867158]
We formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos.
SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration.
Our experiments show that GSR is a challenging novel task, which opens the field for future research.
arXiv Detail & Related papers (2024-04-17T12:53:45Z) - No Bells, Just Whistles: Sports Field Registration by Leveraging Geometric Properties [16.278222277579655]
We propose a novel calibration pipeline enabling camera calibration using a 3D soccer field model and extending the process to assess the multiple-view nature of broadcast videos.
Our method demonstrates superior performance in both multiple- and single-view 3D camera calibration while maintaining competitive results in homography estimation.
arXiv Detail & Related papers (2024-04-12T11:15:15Z) - Context-Aware 3D Object Localization from Single Calibrated Images: A
Study of Basketballs [1.809206198141384]
We present a novel method for 3D basketball localization from a single calibrated image.
Our approach predicts the object's height in pixels in image space by estimating its projection onto the ground plane within the image.
The 3D coordinates of the ball are then reconstructed by exploiting the known projection matrix.
arXiv Detail & Related papers (2023-09-07T11:14:02Z) - Towards Active Learning for Action Spotting in Association Football
Videos [59.84375958757395]
Analyzing football videos is challenging and requires identifying subtle and diverse-temporal patterns.
Current algorithms face significant challenges when learning from limited annotated data.
We propose an active learning framework that selects the most informative video samples to be annotated next.
arXiv Detail & Related papers (2023-04-09T11:50:41Z) - Sports Video Analysis on Large-Scale Data [10.24207108909385]
This paper investigates the modeling of automated machine description on sports video.
We propose a novel large-scale NBA dataset for Sports Video Analysis (NSVA) with a focus on captioning.
arXiv Detail & Related papers (2022-08-09T16:59:24Z) - SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in
Soccer Videos [62.686484228479095]
We propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each.
The dataset is fully annotated with bounding boxes and tracklet IDs.
Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved.
arXiv Detail & Related papers (2022-04-14T12:22:12Z) - Smart Director: An Event-Driven Directing System for Live Broadcasting [110.30675947733167]
Smart Director aims at mimicking the typical human-in-the-loop broadcasting process to automatically create near-professional broadcasting programs in real-time.
Our system is the first end-to-end automated directing system for multi-camera sports broadcasting.
arXiv Detail & Related papers (2022-01-11T16:14:41Z) - Feature Combination Meets Attention: Baidu Soccer Embeddings and
Transformer based Temporal Detection [3.7709686875144337]
We present a two-stage paradigm to detect what and when events happen in soccer broadcast videos.
Specifically, we fine-tune multiple action recognition models on soccer data to extract high-level semantic features.
This approach achieved the state-of-the-art performance in both two tasks, i.e., action spotting and replay grounding, in the SoccerNet-v2 Challenge.
arXiv Detail & Related papers (2021-06-28T08:00:21Z) - SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of
Broadcast Soccer Videos [71.72665910128975]
SoccerNet-v2 is a novel large-scale corpus of manual annotations for the SoccerNet video dataset.
We release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos.
We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection.
arXiv Detail & Related papers (2020-11-26T16:10:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.