Spatiotemporal based table tennis stroke-type assessment

Aktas, Kadir; Demirel, Mehmet F.; Moor, Marilin; Olesk, Johanna; Özçinar, Çağrı; Anbarjafari, Gholamreza

doi:10.1007/s11760-021-01893-7

Cited by 16 publications

(8 citation statements)

References 28 publications

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“…Convolutional layers in a deep neural network iterate through all the pixels in the input images to extract the features. Due to this, they proved to be very successful in various image analysis tasks [ 14 , 15 , 16 , 17 , 18 ]. Considering our input data, the task at hand can be approached as an image processing task, and the CNN can be used to catch the visual cues.…”

Section: Proposed Methodsmentioning

confidence: 99%

A Comparison of Neural Networks and Center of Gravity in Muon Hit Position Estimation

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Giammanco

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The performance of cosmic-ray tomography systems is largely determined by their tracking accuracy. With conventional scintillation detector technology, good precision can be achieved with a small pitch between the elements of the detector array. Improving the resolution implies increasing the number of read-out channels, which in turn increases the complexity and cost of the tracking detectors. As an alternative to that, a scintillation plate detector coupled with multiple silicon photomultipliers could be used as a technically simple solution. In this paper, we present a comparison between two deep-learning-based methods and a conventional Center of Gravity (CoG) algorithm, used to calculate cosmic-ray muon hit positions on the plate detector using the signals from the photomultipliers. In this study, we generated a dataset of muon hits on a detector plate using the Monte Carlo simulation toolkit GEANT4. We demonstrate that two deep-learning-based methods outperform the conventional CoG algorithm by a significant margin. Our proposed algorithm, Fully Connected Network, produces a 0.72 mm average error measured in Euclidean distance between the actual and predicted hit coordinates, showing great improvement in comparison with CoG, which yields 1.41 mm on the same dataset. Additionally, we investigated the effects of different sensor configurations on performance.

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Section: Proposed Methodsmentioning

confidence: 99%

A Comparison of Neural Networks and Center of Gravity in Muon Hit Position Estimation

Aktas

Kiisk

Giammanco

et al. 2022

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“…In [ 101 ], the authors proposed a multistage deep neural network pipeline for recognizing stroke types of table tennis using Spatio-temporal features, which predicts the final class with different aspects at each stage where RGB image-based, Optical Flow-based, pose-based, and region-of-interest-based methods are used. Outcomes of each stage are then fused to obtain the final prediction on the TTStroke-21 dataset.…”

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confidence: 99%

An overview of Human Action Recognition in sports based on Computer Vision

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Ivašić-Kos

2022

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“…Automated detection methods have been developed to address the issues with the huge amount of data and manual analysis. Deep-learningbased methods hold great importance in detection tasks and perform successfully in various domains [12,[16][17][18]. The proven performance of deep learning techniques directed researchers to study deep neural networks (DNNs) in respect to the animal detection task.…”

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confidence: 99%

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

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Avots

et al. 2022

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Changes in the ungulate population density in the wild has impacts on both the wildlife and human society. In order to control the ungulate population movement, monitoring systems such as camera trap networks have been implemented in a non-invasive setup. However, such systems produce a large number of images as the output, hence making it very resource consuming to manually detect the animals. In this paper, we present a new dataset of wild ungulates which was collected in Latvia. Moreover, we demonstrate two methods, which use RetinaNet and Faster R-CNN as backbones, respectively, to detect the animals in the images. We discuss the optimization of training and impact of data augmentation on the performance. Finally, we show the result of aforementioned tune networks over the real world data collected in Latvia.

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Spatiotemporal based table tennis stroke-type assessment

Cited by 16 publications

References 28 publications

A Comparison of Neural Networks and Center of Gravity in Muon Hit Position Estimation

A Comparison of Neural Networks and Center of Gravity in Muon Hit Position Estimation

An overview of Human Action Recognition in sports based on Computer Vision

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

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