Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network
Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s move...
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| Autor*in: |
Li, Xiaofei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 28(2023), 2 vom: 28. Dez., Seite 1653-1667 |
|---|---|
| Übergeordnetes Werk: |
volume:28 ; year:2023 ; number:2 ; day:28 ; month:12 ; pages:1653-1667 |
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| DOI / URN: |
10.1007/s00500-023-09512-y |
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| 520 | |a Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. | ||
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10.1007/s00500-023-09512-y doi (DE-627)SPR054349664 (SPR)s00500-023-09512-y-e DE-627 ger DE-627 rakwb eng Li, Xiaofei verfasserin aut Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. Basketball (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Yolo (dpeaa)DE-He213 Action recognition (dpeaa)DE-He213 Player detection (dpeaa)DE-He213 Luo, Ronghua aut Islam, Faiz Ul aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 2 vom: 28. Dez., Seite 1653-1667 (DE-627)SPR006469531 nnns volume:28 year:2023 number:2 day:28 month:12 pages:1653-1667 https://dx.doi.org/10.1007/s00500-023-09512-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 2 28 12 1653-1667 |
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10.1007/s00500-023-09512-y doi (DE-627)SPR054349664 (SPR)s00500-023-09512-y-e DE-627 ger DE-627 rakwb eng Li, Xiaofei verfasserin aut Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. Basketball (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Yolo (dpeaa)DE-He213 Action recognition (dpeaa)DE-He213 Player detection (dpeaa)DE-He213 Luo, Ronghua aut Islam, Faiz Ul aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 2 vom: 28. Dez., Seite 1653-1667 (DE-627)SPR006469531 nnns volume:28 year:2023 number:2 day:28 month:12 pages:1653-1667 https://dx.doi.org/10.1007/s00500-023-09512-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 2 28 12 1653-1667 |
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10.1007/s00500-023-09512-y doi (DE-627)SPR054349664 (SPR)s00500-023-09512-y-e DE-627 ger DE-627 rakwb eng Li, Xiaofei verfasserin aut Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. Basketball (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Yolo (dpeaa)DE-He213 Action recognition (dpeaa)DE-He213 Player detection (dpeaa)DE-He213 Luo, Ronghua aut Islam, Faiz Ul aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 2 vom: 28. Dez., Seite 1653-1667 (DE-627)SPR006469531 nnns volume:28 year:2023 number:2 day:28 month:12 pages:1653-1667 https://dx.doi.org/10.1007/s00500-023-09512-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 2 28 12 1653-1667 |
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10.1007/s00500-023-09512-y doi (DE-627)SPR054349664 (SPR)s00500-023-09512-y-e DE-627 ger DE-627 rakwb eng Li, Xiaofei verfasserin aut Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. Basketball (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Yolo (dpeaa)DE-He213 Action recognition (dpeaa)DE-He213 Player detection (dpeaa)DE-He213 Luo, Ronghua aut Islam, Faiz Ul aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 2 vom: 28. Dez., Seite 1653-1667 (DE-627)SPR006469531 nnns volume:28 year:2023 number:2 day:28 month:12 pages:1653-1667 https://dx.doi.org/10.1007/s00500-023-09512-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 2 28 12 1653-1667 |
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10.1007/s00500-023-09512-y doi (DE-627)SPR054349664 (SPR)s00500-023-09512-y-e DE-627 ger DE-627 rakwb eng Li, Xiaofei verfasserin aut Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. Basketball (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Yolo (dpeaa)DE-He213 Action recognition (dpeaa)DE-He213 Player detection (dpeaa)DE-He213 Luo, Ronghua aut Islam, Faiz Ul aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 2 vom: 28. Dez., Seite 1653-1667 (DE-627)SPR006469531 nnns volume:28 year:2023 number:2 day:28 month:12 pages:1653-1667 https://dx.doi.org/10.1007/s00500-023-09512-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 2 28 12 1653-1667 |
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Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network |
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Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network |
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Li, Xiaofei |
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Li, Xiaofei Luo, Ronghua Islam, Faiz Ul |
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10.1007/s00500-023-09512-y |
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tracking and detection of basketball movements using multi-feature data fusion and hybrid yolo-t2lstm network |
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Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network |
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Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract The ability to identify human actions in uncontrolled environments is essential for human–computer interaction, especially in sports, to provide athletes, coaches, and analysts, important information about movement techniques and aid in making well-informed decisions regarding player’s movements. Recognizing player’s actions, particularly in the context of basketball sports remains a challenging task due to issues like complex backgrounds, obstructed actions, and inconsistent lighting conditions. Artificial Intelligence and deep learning has promising applications in basketball movement analysis, as it can help basketball athletes enhance their shooting techniques and accuracy, thereby improving the efficiency of both games and training sessions. However, the traditional deep learning-based feature extraction methods lack robustness due to simple architecture and low efficiency. In this study, a hybrid Yolo-T2FLSTM system is proposed for basketball player’s detection and action recognition. An enhanced Yolo algorithm is employed for detecting players in the frame and the integration of LSTM and fuzzy logic is used to perform the final basketball action classification. The models of VGG 16, VGG 19 and ResNet50 are combined in the backbone of Yolo for multi-feature extraction to establish a multi-feature fusion approach and enhance the performance of basketball player and action recognition. The proposed model is evaluated on different basketball videos and achieved a high recognition rate for player detection and 99.3% accuracy for eight basketball actions. Comparative experiments are carried out under various conditions to validate the robustness of the hybrid Yolo-T2FLSTM model. Results show that the proposed method has a high player detection and action recognition rate as compared to other feature extraction models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network |
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