Accurate real-time ball trajectory estimation with onboard stereo camera system for humanoid ping-pong robot

Liu, Yong; Liu, Liang

doi:10.1016/j.robot.2017.12.004

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Myint et al, propose an algorithm to track a ball, in hope of building an automatic umpiring system [9]. Liu et al, present an idea to track the trajectory of an object in real time with stereo camera and to derive the 3D position of the object captured by the camera [10].…”

Section: Forewordmentioning

confidence: 99%

A Study of Automatic and Real-Time Table Tennis Fault Serve Detection System

Hung

2018

Sports

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Calling a table tennis fault serve has never been easy for umpires, since they can only rely on their intuition. This study presents an algorithm that is able to automatically find the positions of the ball and racket in the images captured by high-speed camera. The trajectory of ball toss is analyzed and the result can be used as the objective basis for the umpire to decide if the serve is legal. This algorithm mainly consists of YCbCr color space processing, morphological processing method, circle Hough transform application, separation of moving and static components in an image sequence using the stable principal component pursuit method. The experiment results show that YCbCr color space provides better performance than HSV color space in recognizing the ball color close to skin tone. It is also demonstrated that the positions of the ball and racket can be successfully located by using the methods of color segmentation and stable principal component pursuit. Lastly, it is hoped that this study will provide more useful information regarding how to identify illegal ball toss in tennis ball game using image processing techniques to other researchers.

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Section: Forewordmentioning

confidence: 99%

A Study of Automatic and Real-Time Table Tennis Fault Serve Detection System

Hung

2018

Sports

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“…Currently, the collection of table tennis balls mainly relies on manual labor, which is time-consuming and strenuous. There is a pressing need for an intelligent device that can automatically and efficiently pick up table tennis balls [1][2][3] .…”

Section: Introductionmentioning

confidence: 99%

An automatic system for picking up table tennis balls based on machine vision

Tan

2023

Fourth International Conference on Signal Processing and Computer Science (SPCS 2023)

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“…The processing speed of the vision system is another key factor because it indirectly affects prediction of the direction of the ball, particularly in prediction methods using physical models. Liu [8] proposed an onboard stereo camera system to estimate ping-pong ball trajectories in real time under the asynchronous observations from different cameras. Graphics processing units (plGPU) have become popular computer components in the recent trend of deep learning in image classification because they make the graphics card less dependent on the central processing unit (CPU) and improve the performance of graphics computing.…”

Section: Introductionmentioning

confidence: 99%

Ball Tracking and Trajectory Prediction for Table-Tennis Robots

Lin

Huang

2020

Sensors

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Sports robots have become a popular research topic in recent years. For table-tennis robots, ball tracking and trajectory prediction are the most important technologies. Several methods were developed in previous research efforts, and they can be divided into two categories: physical models and machine learning. The former use algorithms that consider gravity, air resistance, the Magnus effect, and elastic collision. However, estimating these external forces require high sampling frequencies that can only be achieved with high-efficiency imaging equipment. This study thus employed machine learning to learn the flight trajectories of ping-pong balls, which consist of two parabolic trajectories: one beginning at the serving point and ending at the landing point on the table, and the other beginning at the landing point and ending at the striking point of the robot. We established two artificial neural networks to learn these two trajectories. We conducted a simulation experiment using 200 real-world trajectories as training data. The mean errors of the proposed dual-network method and a single-network model were 39.6 mm and 42.9 mm, respectively. The results indicate that the prediction performance of the proposed dual-network method is better than that of the single-network approach. We also used the physical model to generate 330 trajectories for training and the simulation test results show that the trained model achieved a success rate of 97% out of 30 attempts, which was higher than the success rate of 70% obtained by the physical model. A physical experiment presented a mean error and standard deviation of 36.6 mm and 18.8 mm, respectively. The results also show that even without the time stamps, the proposed method maintains its prediction performance with the additional advantages of 15% fewer parameters in the overall network and 54% shorter training time.

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Accurate real-time ball trajectory estimation with onboard stereo camera system for humanoid ping-pong robot

Cited by 8 publications

References 18 publications

A Study of Automatic and Real-Time Table Tennis Fault Serve Detection System

A Study of Automatic and Real-Time Table Tennis Fault Serve Detection System

An automatic system for picking up table tennis balls based on machine vision

Ball Tracking and Trajectory Prediction for Table-Tennis Robots

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