Jetson, Where Is the Ball? Using Neural Networks for Ball Detection at RoboCup 2017

Gabel, Alexander; Heuer, Tanja; Schiering, Ina; Gerndt, Reinhard

doi:10.1007/978-3-030-27544-0_15

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…The RoboCup Soccer League is popular in humanoid research due to the visual identification and localization challenges it presents. [45,46] and [47] are some examples of real-time, CNN based ball detection approaches utilizing RGB cameras developed specifically for RoboCup. Cruz et al [48] could additionally estimate player poses, goal locations and other key pitch features using intensity images alone.…”

Section: Localization Mapping and Slammentioning

confidence: 99%

See 1 more Smart Citation

Perception for Humanoid Robots

Roychoudhury,

Khorshidi,

Agrawal

et al. 2023

Curr Robot Rep

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Purpose of Review The field of humanoid robotics, perception plays a fundamental role in enabling robots to interact seamlessly with humans and their surroundings, leading to improved safety, efficiency, and user experience. This scientific study investigates various perception modalities and techniques employed in humanoid robots, including visual, auditory, and tactile sensing by exploring recent state-of-the-art approaches for perceiving and understanding the internal state, the environment, objects, and human activities. Recent Findings Internal state estimation makes extensive use of Bayesian filtering methods and optimization techniques based on maximum a-posteriori formulation by utilizing proprioceptive sensing. In the area of external environment understanding, with an emphasis on robustness and adaptability to dynamic, unforeseen environmental changes, the new slew of research discussed in this study have focused largely on multi-sensor fusion and machine learning in contrast to the use of hand-crafted, rule-based systems. Human robot interaction methods have established the importance of contextual information representation and memory for understanding human intentions. Summary This review summarizes the recent developments and trends in the field of perception in humanoid robots. Three main areas of application are identified, namely, internal state estimation, external environment estimation, and human robot interaction. The applications of diverse sensor modalities in each of these areas are considered and recent significant works are discussed.

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Section: Localization Mapping and Slammentioning

confidence: 99%

“…Cruz et al [48] could additionally estimate player poses, goal locations and other key pitch features using intensity images alone. Due to the low on-board computational power of the humanoids, others have used fast, low power external mobile GPU boards such as the Nvidia Jetson to aid inference [47,49].…”

Section: Localization Mapping and Slammentioning

confidence: 99%

Perception for Humanoid Robots

Roychoudhury,

Khorshidi,

Agrawal

et al. 2023

Curr Robot Rep

View full text Add to dashboard Cite

show abstract

“…Then, individual results are combined to produce a final ball probability map. (Gabel et al, 2018) uses off-the-shelf deep neural network-based classifier architectures (AlexNet and Inception) fine tuned to detect the ball in rectangular patches cropped from the input image. Authors reported 99% ball detection accuracy on the custom dataset from RoboCup competition.…”

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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“…More importantly, the quality of the object recognition system largely depends on the efficiency of the algorithm used to generate candidates for classification (whether CNNS are used for binary classification tasks [29] or to detect several relevant object categories [30], or to detect robots (humanoids) [29]). Obviously, ball recognition has received the most attention [31]. Ball-only CNNs had input size massively reduced to be ported to typical robots for the humanoid league [32].…”

Section: Human Vision and Computer Visionmentioning

confidence: 99%

Human-In-The-Loop Construction of Decision Tree Classifiers with Parallel Coordinates

Estivill‐Castro

Gilmore

Hexel

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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How can there be Human-In-the-Loop-Learning (HILL) if datasets aimed at building classifiers have ever more dimensions? We make two contributions. First, we examine the few early results on the effectiveness of HILL for building autonomous classifiers and report on our own experiment that validates the merits of HILL. Second, we introduce a HILL system (by using parallel coordinates) for learning of decision tree classifiers (DTCs). DTCs importantly emphasise the relevance of attributes and enable attribute selection, and therefore are appreciated for their transparency.The proposed system addresses a number of the shortcomings of the many HILL systems and allows for easy exploration of datasets. In particular, we incorporate parallel coordinates effectively in our tool for visualisation of high dimensional datasets. We can not only focus the learning on the accuracy of classifiers, but we can enhance performance in other important factors such as system's interpretability and the ability to gain insight into datasets. Finally, we show the advantages of our HILL system in the application area of mobile robotics using the case study of image segmentation in robotic soccer.

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Jetson, Where Is the Ball? Using Neural Networks for Ball Detection at RoboCup 2017

Cited by 9 publications

References 12 publications

Perception for Humanoid Robots

Perception for Humanoid Robots

FootAndBall: Integrated Player and Ball Detector

Human-In-The-Loop Construction of Decision Tree Classifiers with Parallel Coordinates

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