A Hybrid Approach for Tracking Individual Players in Broadcast Match
Videos
- URL: http://arxiv.org/abs/2003.03271v2
- Date: Tue, 10 Mar 2020 13:09:14 GMT
- Title: A Hybrid Approach for Tracking Individual Players in Broadcast Match
Videos
- Authors: Roberto L. Castro, Diego Andrade, Basilio Fraguela
- Abstract summary: This paper introduces a player tracking solution which is both fast and accurate.
The approach combines several models that are executed concurrently in a relatively modest hardware.
As for performance, our proposal can process high definition videos (1920x1080) at 80 fps.
- Score: 1.160208922584163
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tracking people in a video sequence is a challenging task that has been
approached from many perspectives. This task becomes even more complicated when
the person to track is a player in a broadcasted sport event, the reasons being
the existence of difficulties such as frequent camera movements or switches,
total and partial occlusions between players, and blurry frames due to the
codification algorithm of the video. This paper introduces a player tracking
solution which is both fast and accurate. This allows to track a player
precisely in real-time. The approach combines several models that are executed
concurrently in a relatively modest hardware, and whose accuracy has been
validated against hand-labeled broadcast video sequences. Regarding the
accuracy, the tests show that the area under curve (AUC) of our approach is
around 0.6, which is similar to generic state of the art solutions. As for
performance, our proposal can process high definition videos (1920x1080 px) at
80 fps.
Related papers
- SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame
Interpolation [11.198172694893927]
SportsSloMo is a benchmark consisting of more than 130K video clips and 1M video frames of high-resolution ($geq$720p) slow-motion sports videos crawled from YouTube.
We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets.
We introduce two loss terms considering the human-aware priors, where we add auxiliary supervision to panoptic segmentation and human keypoints detection.
arXiv Detail & Related papers (2023-08-31T17:23:50Z) - Towards AI enabled automated tracking of multiple boxers [0.7067443325368975]
Continuous tracking of boxers across multiple training sessions helps quantify traits required for the well-known ten-point-must system.
This work summarizes our progress in creating a system in an economically single fixed top-view camera.
Specifically, we describe improved algorithm for bout transition detection and in-bout continuous player identification without erroneous ID updation or ID switching.
arXiv Detail & Related papers (2023-08-09T16:46:21Z) - A Graph-Based Method for Soccer Action Spotting Using Unsupervised
Player Classification [75.93186954061943]
Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences.
In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs.
For the player identification task, our method obtains an overall performance of 57.83% average-mAP by combining it with other modalities.
arXiv Detail & Related papers (2022-11-22T15:23:53Z) - Graph-Based Multi-Camera Soccer Player Tracker [1.6244541005112743]
The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field.
The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions.
Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance.
arXiv Detail & Related papers (2022-11-03T20:01:48Z) - P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos [64.57435509822416]
This work consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads.
We formulate two sets of action detection problems -- emphaction localization and emphaction recognition.
The results confirm that TheName is still a challenging task and can be used as a special benchmark for dense action detection from videos.
arXiv Detail & Related papers (2022-07-26T08:34:17Z) - 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) - Semi-Supervised Action Recognition with Temporal Contrastive Learning [50.08957096801457]
We learn a two-pathway temporal contrastive model using unlabeled videos at two different speeds.
We considerably outperform video extensions of sophisticated state-of-the-art semi-supervised image recognition methods.
arXiv Detail & Related papers (2021-02-04T17:28:35Z) - AIM 2020 Challenge on Video Temporal Super-Resolution [118.46127362093135]
Second AIM challenge on Video Temporal Super-Resolution (VTSR)
This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR)
arXiv Detail & Related papers (2020-09-28T00:10:29Z) - AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and
Results [129.15554076593762]
This paper reviews the first AIM challenge on video temporal super-resolution (frame)
From low-frame-rate (15 fps) video sequences, the challenge participants are asked to submit higher-framerate (60 fps) video sequences.
We employ the REDS VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes.
arXiv Detail & Related papers (2020-05-04T01:51:23Z) - Unsupervised Temporal Feature Aggregation for Event Detection in
Unstructured Sports Videos [10.230408415438966]
We study the case of event detection in sports videos for unstructured environments with arbitrary camera angles.
We identify and solve two major problems: unsupervised identification of players in an unstructured setting and generalization of the trained models to pose variations due to arbitrary shooting angles.
arXiv Detail & Related papers (2020-02-19T10:24:22Z)
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.