Upper Body Pose Estimation for Team Sports Videos Using a Poselet-Regressor of Spine Pose and Body Orientation Classifiers Conditioned by the Spine Angle Prior

Oshima, Kyoko; Tanabiki, Masamoto; Aoki, Yoshimitsu

doi:10.2197/ipsjtcva.7.121

Cited by 6 publications

(3 citation statements)

References 57 publications

(160 reference statements)

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“…The presented results of both papers show how the head-and upper-body-pose is leveraged for individual players, resulting in 3D visualizations that could be then studied by coaches. Later, the same authors [33] extended their previous work by training a poselet-regressor that produces an accurate estimation of the spine pose. By introducing priors based on the spine angle, several body classifiers are trained, which result in a coarse orientation of the upper body; note that, once the head region of each player is detected, the contribution of the presented regressor is to estimate the relative pelvis location for each target.…”

Section: A Head-pose-gaze Estimation In Sportsmentioning

confidence: 99%

Towards soccer pass feasibility maps: the role of players’ orientation

Arbués-Sangüesa

Martín

Fernández

et al. 2021

Journal of Sports Sciences

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Once player tracking has been established as one of the main data sources in soccer, many challenges have emerged for data scientists, who attempt to recognize patterns from 2D trajectories in order to build tools that might help coaches to improve the performance of their teams. For instance, pass models predict where the ball should go next during pass events.However, existing models are mainly fed with players' location and prior data, hence omitting critical pieces of information such as players' body orientation. This paper presents a computational model to obtain pass feasibility maps, where player orientation is exploited and analyzed. As a matter of fact, orientation proves to be crucial when modelling field-of-view and correct positioning of players, since it limits the potential receiving area of all candidates. Different proposals are given to evaluate the proposed pass feasibility map, reaching 0.46 and 0.79 in Top1 and Top3 accuracy, respectively, with a +0.2 boost obtained after merging positional data with orientation.

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Section: A Head-pose-gaze Estimation In Sportsmentioning

confidence: 99%

Towards soccer pass feasibility maps: the role of players’ orientation

Arbués-Sangüesa

Martín

Fernández

et al. 2021

Journal of Sports Sciences

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“…Hayasahi et al [16] proposed a CNN model visual imagery analyzer to recognize the upper body pose features and use the head and pelvis to get the spine orientation and estimate body orientation to estimate the random decision of human movement. Jian et al [40] developed an AI coach camera system to collect the 'great' pose of athlete in the spatialtemporal sequence.…”

Section: Postural Assessment Using Markerless-based Approachmentioning

confidence: 99%

Markerless gait estimation and tracking for postural assessment

Tay

Lim

Phang

2022

Multimed Tools Appl

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Postural assessment is crucial in the sports screening system to reduce the risk of severe injury. The capture of the athlete’s posture using computer vision attracts huge attention in the sports community due to its markerless motion capture and less interference in the physical training. In this paper, a novel markerless gait estimation and tracking algorithm is proposed to locate human key-points in spatial-temporal sequences for gait analysis. First, human pose estimation using OpenPose network to detect 14 core key-points from the human body. The ratio of body joints is normalized with neck-to-pelvis distance to obtain camera invariant key-points. These key-points are subsequently used to generate a spatial-temporal sequences and it is fed into Long-Short-Term-Memory network for gait recognition. An indexed person is tracked for quick local pose estimation and postural analysis. This proposed algorithm can automate the capture of human joints for postural assessment to analyze the human motion. The proposed system is implemented on Intel Up Squared Board and it can achieve up to 9 frames-per-second with 95% accuracy of gait recognition.

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“…As presented at five workshops on computer vision in sports held at CVPR or ICCV since 2013, many vision based approaches 18) were proposed to analyze ball possession 19)20) , ball/player trajectory 21) , player identification 22) , pose estimation for sports action recognition 4) and activity recognition in sports applications 3) . The papers in the workshops also reported that a deep learning approach like 23) was particularly valuable.…”

Section: Sports Analysismentioning

confidence: 99%

[Papers] Keep Your Eye on the Ball: Detection of Kicking Motions in Multi-view 4K Soccer Videos

Tasaka

2020

MTA

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For automated game analysis, it is essential to detect the kicking motions of players in soccer videos in order to understand each player's actions. This paper presents a fast and accurate approach to detecting kicking motions with a ball-centric window in multi-view 4K soccer videos. Based on powerful object detection techniques like SSD or YOLOv3 and pose estimation techniques like OpenPose or CPN, we propose novel solutions to overcome two challenges in 4K soccer videos. The first challenge is that it is basically too computationally heavy to process the massive amount of data in multi-view 4K videos. The solution to this challenge is that we only process a small portion (i.e. a ball-centric window) of 4K video, benefiting from an object tracking technique and homography transformation. The second challenge is that kicking motions may be incorrectly detected due to two factors. One is the absence of depth information and the other is the inaccuracy of pose estimation. We fuse multiple views to avoid the depth problem. In addition, we propose enlarging the person areas to effectively improve the accuracy of pose estimation. The experiments on real data from the J1 League demonstrate that the proposed approach achieves both faster and more accurate detection of kicking motions than conventional methods.

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Upper Body Pose Estimation for Team Sports Videos Using a Poselet-Regressor of Spine Pose and Body Orientation Classifiers Conditioned by the Spine Angle Prior

Cited by 6 publications

References 57 publications

Towards soccer pass feasibility maps: the role of players’ orientation

Towards soccer pass feasibility maps: the role of players’ orientation

Markerless gait estimation and tracking for postural assessment

[Papers] Keep Your Eye on the Ball: Detection of Kicking Motions in Multi-view 4K Soccer Videos

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