A deep learning ball tracking system in soccer videos

Kamble, Paresh R.; Keskar, Avinash G.; Bhurchandi, Kishor M.

doi:10.1016/j.opelre.2019.02.003

Cited by 58 publications

(31 citation statements)

References 45 publications

(67 reference statements)

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“…Finally refer to AI and computer vision works, Thomas et al [49] discusses a selection of current commercial applications that use computer vision for sports analysis, and highlights some of the topics that are currently being addressed in the research community. Kamble et al [50] present a novel deep learning approach for 2D ball detection and tracking (DLBT) in soccer videos, posing various challenges.…”

Section: Related Workmentioning

confidence: 99%

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Haptic Glove TV Device for People with Visual Impairment

Villamarín

Menéndez

2021

Sensors

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Immersive video is changing the way we enjoy TV. It is no longer just about receiving sequential images with audio, but also playing with other human senses through smells, vibrations of movement, 3D audio, feeling water, wind, heat, and other emotions that can be experienced through all human senses. This work aims to validate the usefulness of an immersive and interactive solution for people with severe visual impairment by developing a haptic glove that allows receiving signals and generating vibrations in hand, informing about what happens in a scene. The study case presented here shows how the haptic device can take the information about the ball’s location in the playing field, synchronized with the video reception, and deliver it to the user in the form of vibrations during the re-transmission of a soccer match. In this way, we take visually impaired people to live a new sensory experience, allowing digital and social inclusion and accessibility to audiovisual technologies that they could not enjoy before. This work shows the methodology used for the design, implementation, and results evaluation. Usability tests were carried out with fifteen visually impaired people who used the haptic device to attend a soccer match synchronized with the glove’s vibrations.

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Section: Related Workmentioning

confidence: 99%

“…However, it remains a new line of research that could be proposed to analyze and determine which would be the most feasible and efficient. On Section 2 we mentioned two related works [49,50].…”

Section: Integration Of the Haptic Glove With The Televisionmentioning

confidence: 99%

Haptic Glove TV Device for People with Visual Impairment

Villamarín

Menéndez

2021

Sensors

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“…Also off-the-shelf architectures, such as Inception, are powerful but require much more computational resources for training and inference . (Kamble et al, 2019) describes deep learning approach for 2D ball and player detection and tracking in soccer videos. First, median filteringbased background subtraction is used to detect moving objects.…”

Section: Related Workmentioning

confidence: 99%

FootAndBall: Integrated Player and Ball Detector

Komorowski

Kurzejamski

Sarwas

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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The paper describes a deep neural network-based detector dedicated for ball and players detection in high resolution, long shot, video recordings of soccer matches. The detector, dubbed FootAndBall, has an efficient fully convolutional architecture and can operate on input video stream with an arbitrary resolution. It produces ball confidence map encoding the position of the detected ball, player confidence map and player bounding boxes tensor encoding players' positions and bounding boxes. The network uses Feature Pyramid Network desing pattern, where lower level features with higher spatial resolution are combined with higher level features with bigger receptive field. This improves discriminability of small objects (the ball) as larger visual context around the object of interest is taken into account for the classification. Due to its specialized design, the network has two orders of magnitude less parameters than a generic deep neural network-based object detector, such as SSD or YOLO. This allows real-time processing of high resolution input video stream.

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“…What is worse, many ball-like objects are easily detected as ball because very limited appearance features can be used to be distinguished. Recently, many ball detection methods are designed based on deep learning [31].…”

Section: D Ball Detectionmentioning

confidence: 99%

Multi‐camera 3D ball tracking framework for sports video

Wu¹,

Liang³

et al. 2020

IET Image Processing

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Accurate ball tracking in sports is vital for automatic sports analysis yet it is challenging mainly due to the small size and occlusions. This study proposes a novel multi-camera 3D ball tracking (MBT) framework for sports video. The proposed framework consists of four parts: 2D ball detection, 2D ball tracking, 3D position fusion, and 3D ball tracking. In 2D aspect, the multi-scale features are introduced to enhance the 2D ball detection, and the 2D ball tracking is also improved by exploring cross-view information to handle the occlusion and timely updating tracking model with detection results to alleviate the problem of tracking drift. For 3D ball, a novel 3D position fusion method is proposed to optimise the ball position and the 3D ball tracking approach with improved Kalman filter is finally applied to ensure a smooth 3D ball trajectory. Moreover, compared to the existing products in commercial, the proposed framework does not require any special equipment and is thus low cost. Extensive experiments for 2D and 3D ball on a public dataset demonstrate that the proposed framework is robust to ball tracking in sports video, even in the presence of environmental interferences, substantial occlusions, and even calibration errors.

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A deep learning ball tracking system in soccer videos

Cited by 58 publications

References 45 publications

Haptic Glove TV Device for People with Visual Impairment

Haptic Glove TV Device for People with Visual Impairment

FootAndBall: Integrated Player and Ball Detector

Multi‐camera 3D ball tracking framework for sports video

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