FPGA-Based Vision Processing System for Automatic Online Player Tracking in Indoor Sports

Ibraheem, Omar W.; Irwansyah, Arif; Hagemeyer, Jens; Porrmann, Mario; Rueckert, Ulrich

doi:10.1007/s11265-018-1381-8

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…However, it should be noted that in the actual use of camera equipment, due to differences in specific scenarios, and mobile and fixed [7]. For many of the current algorithms for real-time simultaneous tracking of multiple targets, its size is relatively small, there is serious obscuration or poor image quality of the target, in the specific tracking effect, but not ideal, and tracking content is not comprehensive, as there is a lack of real time [8].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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The effect is tested in various specific scenes of sports videos to complete the multitarget motion multitarget tracking detection application applicable to various specific scenes within sports videos. In this paper, deep neural networks are applied to sports video multitarget motion shadow suppression and accurate tracking to improve tracking performance. After the target frame selection is determined, the tracker uses an optical flow method to estimate the limits of the target sports video multitarget motion based on the sports video multitarget motion of the target object between frames. The detector first scans each sports video image frame one by one, observing the previously discovered and learned image frame subregions one by one until the current moment that is highly like the target to be tracked. The preprocessed remote sensing images are converted into grayscale images, the histogram is normalized, and the appropriate height threshold is selected in combination with the regional growth function to realize the rejection of sports video multitarget motion shadow and establish the sports video multitarget network model. The distance and direction of the precise target displacement are determined by frequency-domain vectors and null domain vectors, and the target action judgment mechanism is formed by decision learning. Finally, comparing with other shadow rejection and precision tracking algorithms, the proposed algorithm achieves greater advantages in terms of accuracy and time consumption.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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“…Those methods can be generally categorized as either video/computer vision-based methods or using wearable devices. Video/computer vision-based methods rely on the video camera(s), depth camera(s) or motion sensing technology (e. g. Microsoft Kinect) to capture the motion of players, and the activities are classified through image processing algorithms where distinguishable features are extracted from video clips or images [7][8][9][10]. Video/computer vision-based methods have some inherent limitations.…”

Section: Introductionmentioning

confidence: 99%

Basketball Activity Classification Based on Upper Body Kinematics and Dynamic Time Warping

2020

Int J Sports Med

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Basketball activity classification can help document players’ statistics, allow coaches, trainers and the medical team to quantitatively supervise players’ physical exertion and optimize training strategy, and further help prevent potential injuries. Traditionally, sports activity classification was done by manual notational, or through multi-camera systems or motion sensing technology. These methods were often erroneous and limited by space. This study presents a basketball activity classification model based on Dynamic Time Warping (DTW) and body kinematic measures. Twenty participants, including 10 experienced players and 10 novice players, were involved in an experimental study. The experienced and novice players differed in their years of playing basketball. Four basketball movements, including shooting, passing, dribbling, and lay-up were classified by kinematic measures. The results indicate that the proposed model can successfully classify different basketball movements with high accuracy and efficiency. Specifically, with the resultant acceleration of the hand, this model can achieve classification precision, recall, and specificity up to 0.984, 0.983 and 0.994, respectively. Findings from this study supported the feasibility of using DTW in real-time sports activity classification and provided insights into the optimal sensor placement for basketball activity classification applications.

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Design and Implementation of Image Sensor Data Capture Based on FPGA

Krainyk

2023

SN COMPUT. SCI.

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FPGA-Based Vision Processing System for Automatic Online Player Tracking in Indoor Sports

Cited by 4 publications

References 29 publications

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Basketball Activity Classification Based on Upper Body Kinematics and Dynamic Time Warping

Design and Implementation of Image Sensor Data Capture Based on FPGA

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