Pose estimation of soccer players using multiple uncalibrated cameras

Afrouzian, Reza; Seyedarabi, Hadi; Kasaei, Shohreh

doi:10.1007/s11042-015-2611-8

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…Based on the available data-sensor and video data-we explored two analysis techniques that detect rope pulling-related events. First, a hybrid approach, in which initially by means of person 1 https://www.movesense.com 2 https://apps.apple.com/us/app/movesense-showcase/id1439876677 detection, we identify the belayer and the climber, see Fig. 1b.…”

Section: Methodsmentioning

confidence: 99%

“…In [13], the authors extracted temporal human 2D pose sequences from video frames, followed by automatic event detection in the athlete's motion using convolutional neural networks (CNN). Similarly [1] and [10,20], implemented pose estimation based on the estimation of the skeleton of every person in an image in a soccer match and climbing, respectively. In [2], players in a soccer match are represented with blobs, i.e., regions segmented out from the playfield by color differentiating.…”

Section: Related Workmentioning

confidence: 99%

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Video and Sensor-Based Rope Pulling Detection in Sport Climbing

Ivanova

Andrić

Moaveninejad

et al. 2020

Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports

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Sport climbing is becoming an increasingly popular competitive sport as well as a recreational activity. For this reason, indoor sport climbing operators are constantly trying to improve their services and optimally use their infrastructure. One way to support such a task is to track the climbing activities performed by visitors while climbing. This paper considers a scenario in which a sensor is attached to a piece of climbing equipment that connects the climbing rope to the bolt anchors (quickdraws) and a camera is overlooking a climbing wall. Within this scenario, this paper explores two approaches to detect when a climber finishes a climb and pulls the rope from the wall: 1) a hybrid approach in which sensors and cameras are used and 2) a video-based approach where only cameras are used. The evaluation resulted in recognition precision of 91% for the hybrid and 76% for the video-based approach, respectively. This paper also discusses advantages and disadvantages of analysed approaches and points out future research directions to allow the automatic tracking of climbing activities.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Video and Sensor-Based Rope Pulling Detection in Sport Climbing

Ivanova

Andrić

Moaveninejad

et al. 2020

Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports

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“…et al [4] built up an HPE-based athletic training assistance system to detect bad poses from a sequence of 2D users' poses. In addition, HPE-based approach is applied to capture players' motion in sports like badminton [116], soccer [134], [135], and tennis [136], [137]. In conclusion, HPE-based sports analysis mainly relies on a comparison between a player and an exemplar.…”

Section: Hpe-based Sports Analysis By Comparisons Between Players And...mentioning

confidence: 99%

Vision-Based Human Pose Estimation via Deep Learning: A Survey

Lan

et al. 2023

IEEE Trans. Human-Mach. Syst.

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Human Pose Estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains such as human-computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in the literature. In this paper, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2D and 3D HPE, and their applications, discuss the challenges and the research trends through bibliometrics and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.

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“…The use of 3D cameras is essential to ensure that the relevant data or information that is needed is captured and filtered as per the specifications [106]. Filtering of such data is through the use of editing features that are necessary for the overall identification of needed data.…”

Section: Film Editingmentioning

confidence: 99%

3D Face Tracking Using Stereo Cameras: A Review

et al. 2020

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This review considers previous research, regarding the background and applications of 3D facetracking systems with a focus on stereo camera-based systems. Stereo cameras are less expensive than laser ranging systems, and they are widely available on devices such as smart phones. This review aims to spur further development and applications of face tracking in this domain. Many studies on face tracking have used concepts such as the Kanade-Lucas-Tomasi method, particle filters, tracking-learning-detecting, probability hypothesis density, mean shift/cam shift, and others. As imaging constraints are relaxed, facial tracking becomes more challenging. This review presents an exposition of the most common challenges in face tracking, such as occlusion and clutter, pose variations, changes in facial resolution, illumination variations, and facial deformation. Five forms of pose estimation are discussed: appearance template methods, detector arrays, flexible models, geometric methods, and tracking methods. Applications of the listed 3D face tracking systems are also discussed, including face modelling, film editing, access control, security, and surveillance.

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Pose estimation of soccer players using multiple uncalibrated cameras

Cited by 11 publications

References 26 publications

Video and Sensor-Based Rope Pulling Detection in Sport Climbing

Video and Sensor-Based Rope Pulling Detection in Sport Climbing

Vision-Based Human Pose Estimation via Deep Learning: A Survey

3D Face Tracking Using Stereo Cameras: A Review

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