Recognition of basketball referee signals from real-time videos

Žemgulys, Julius; Raudonis, Vidas; Maskeliūnas, Rytis; Damaševičius, Robertas

doi:10.1007/s12652-019-01209-1

Cited by 35 publications

(18 citation statements)

References 43 publications

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“…(1) plants [10], (2) car brands and models [11], and (3) insects (the Formicidae ants genera) [5]. With emerging consumer-level technologies, e.g., 3D scanning and visual odometry from image series [12][13][14][15], we expect an increasing number of collection-based datasets and rising interest in multi-view classification.…”

Section: Introductionmentioning

confidence: 99%

Multi-view classification with convolutional neural networks

Seeland

Mäder

2021

PLoS ONE

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Humans’ decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object’s class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision. We propose a classification scheme that relies on fusing visual information captured through images depicting the same object from multiple perspectives. Convolutional neural networks are used to extract and encode visual features from the multiple views and we propose strategies for fusing these information. More specifically, we investigate the following three strategies: (1) fusing convolutional feature maps at differing network depths; (2) fusion of bottleneck latent representations prior to classification; and (3) score fusion. We systematically evaluate these strategies on three datasets from different domains. Our findings emphasize the benefit of integrating information fusion into the network rather than performing it by post-processing of classification scores. Furthermore, we demonstrate through a case study that already trained networks can be easily extended by the best fusion strategy, outperforming other approaches by large margin.

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Section: Introductionmentioning

confidence: 99%

Multi-view classification with convolutional neural networks

Seeland

Mäder

2021

PLoS ONE

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“…We used a shot arm model similar to the two-dimensional three-section model of [27]. It is assumed that the shooting arm moves in the vertical plane and has three rigid linkages, where the upper arm, forearm, and hand have rotational joints, as well as shoulder, elbow, and wrist joints.…”

Section: Projectile Arm Geometrymentioning

confidence: 99%

[Retracted] CNN‐LSTM Hybrid Model for Kinematic Feature Analysis and Parabolic Radian Prediction in Basketball Videos

Jiang

Chen

Yang

2021

Computational Intelligence and Neuroscience

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With the improvement of living standards around the world, people's love for sports has also increased; basketball is especially loved by people. It is of great importance to provide sound motor instruction for basketball. To this end, this paper comprehensively investigates the dependence between the optimal release conditions and the corresponding shooting arm movements in basketball players. We carry out kinematic feature analysis of basketball sports videos, propose a hybrid CNN-LSTM model that can predict the arc of the shooting parry, and identify the key movements of the arm joint that produce optimal release velocity, angle, and backspin in short-, mid-, and long-range shots. The experiment demonstrates that the model has three rigid planar links with rotational joints that mimic the shoulder, elbow, and wrist joints of the upper arm, forearm, and hand, which are better at guiding the optimal ball release speed, angle, and backspin for different players with the fastest ball speed being about 4.6 m/s and the slowest being about 1.7 m/s.

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“…The significance of multifaceted applications of hand gestures has increased in the industry and research fields [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Computer vision, pattern recognition, and human-computer interaction (HCI) are among the popular areas that involve 3D hand-gesture modeling [ 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Mishra

Kim

Cha

et al. 2021

Sensors

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An authorized traffic controller (ATC) has the highest priority for direct road traffic. In some irregular situations, the ATC supersedes other traffic control. Human drivers indigenously understand such situations and tend to follow the ATC; however, an autonomous vehicle (AV) can become confused in such circumstances. Therefore, autonomous driving (AD) crucially requires a human-level understanding of situation-aware traffic gesture recognition. In AVs, vision-based recognition is particularly desirable because of its suitability; however, such recognition systems have various bottlenecks, such as failing to recognize other humans on the road, identifying a variety of ATCs, and gloves in the hands of ATCs. We propose a situation-aware traffic control hand-gesture recognition system, which includes ATC detection and gesture recognition. Three-dimensional (3D) hand model-based gesture recognition is used to mitigate the problem associated with gloves. Our database contains separate training and test videos of approximately 60 min length, captured at a frame rate of 24 frames per second. It has 35,291 different frames that belong to traffic control hand gestures. Our approach correctly recognized traffic control hand gestures; therefore, the proposed system can be considered as an extension of the operational domain of the AV.

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Recognition of basketball referee signals from real-time videos

Cited by 35 publications

References 43 publications

Multi-view classification with convolutional neural networks

Multi-view classification with convolutional neural networks

[Retracted] CNN‐LSTM Hybrid Model for Kinematic Feature Analysis and Parabolic Radian Prediction in Basketball Videos

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

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