{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T12:52:02Z","timestamp":1782132722520,"version":"3.54.5"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Zhejiang Provincial Natural Science Foundation"},{"name":"Jijun Tong, the Zhejiang Provincial Natural Science Foundation"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s11554-026-01906-z","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T12:16:29Z","timestamp":1782130589000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TGMA-Net: a lightweight spatiotemporal attention network for precise real-time tennis tracking"],"prefix":"10.1007","volume":"23","author":[{"given":"Lin","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jijun","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"1906_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106839","volume":"181","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Pan, H., Wang, J.: Enabling deformation slack in tracking with temporally even correlation filters. Neural Netw. 181, 106839 (2025). https:\/\/doi.org\/10.1016\/j.neunet.2024.106839","journal-title":"Neural Netw."},{"key":"1906_CR2","doi-asserted-by":"publisher","unstructured":"Huang, Y.-C., Liao, I.-N., Chen, C.-H., \u0130k, T.-U., Peng, W.-C.: Tracknet: A deep learning network for tracking high-speed and tiny objects in sports applications. In 2019 16th IEEE international conference on advanced video and signal based surveillance (AVSS), pages 1\u20138. IEEE, (2019). https:\/\/doi.org\/10.1109\/avss.2019.8909871","DOI":"10.1109\/avss.2019.8909871"},{"key":"1906_CR3","doi-asserted-by":"publisher","unstructured":"Sun, N.-E., Lin, Y.-C., Chuang, S.-P., Hsu, T.-H., Yu, D.-R., Chung, H.-Y., \u0130k, T.-U.:. Tracknetv2: Efficient shuttlecock tracking network. In 2020 International Conference on Pervasive Artificial Intelligence (ICPAI), pages 86\u201391. IEEE, 2020. https:\/\/doi.org\/10.1109\/icpai51961.2020.00023","DOI":"10.1109\/icpai51961.2020.00023"},{"key":"1906_CR4","doi-asserted-by":"publisher","unstructured":"Chen, Y.-J., Wang, Y.-S.: Tracknetv3: Enhancing shuttlecock tracking with augmentations and trajectory rectification. In Proceedings of the 5th ACM International Conference on Multimedia in Asia, pages 1\u20137, (2023). https:\/\/doi.org\/10.1145\/3595916.3626370","DOI":"10.1145\/3595916.3626370"},{"key":"1906_CR5","doi-asserted-by":"publisher","unstructured":"Raj, A., Wang, L., Gedeon, T.: Tracknetv4: Enhancing fast sports object tracking with motion attention maps. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1\u20135. IEEE, (2025). https:\/\/doi.org\/10.1109\/icassp49660.2025.10889364","DOI":"10.1109\/icassp49660.2025.10889364"},{"issue":"6","key":"1906_CR6","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11554-025-01789-6","volume":"22","author":"Y Nie","year":"2025","unstructured":"Nie, Y., Li, G., Fan, Y., Wang, F.: Eght: a lightweight spatiotemporal fusion and dynamic attention model for real-time badminton detection. J. Real-Time Image Proc. 22(6), 211 (2025). https:\/\/doi.org\/10.1007\/s11554-025-01789-6","journal-title":"J. Real-Time Image Proc."},{"key":"1906_CR7","doi-asserted-by":"publisher","unstructured":"Voeikov, R., Falaleev, N., Baikulov, R.: Ttnet: Real-time temporal and spatial video analysis of table tennis. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pages 884\u2013885, (2020). https:\/\/doi.org\/10.1109\/cvprw50498.2020.00450","DOI":"10.1109\/cvprw50498.2020.00450"},{"key":"1906_CR8","doi-asserted-by":"publisher","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et\u00a0al.: Searching for mobilenetv3. In Proceedings of the IEEE\/CVF international conference on computer vision, pages 1314\u20131324, (2019). https:\/\/doi.org\/10.1109\/iccv.2019.00140","DOI":"10.1109\/iccv.2019.00140"},{"key":"1906_CR9","doi-asserted-by":"publisher","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV) , 116\u2013131 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"1906_CR10","doi-asserted-by":"publisher","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Chunjing, X., Chang, X.: Ghostnet: More features from cheap operations. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 1580\u20131589 (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.00165","DOI":"10.1109\/cvpr42600.2020.00165"},{"key":"1906_CR11","doi-asserted-by":"publisher","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 13733\u201313742 (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01352","DOI":"10.1109\/cvpr46437.2021.01352"},{"key":"1906_CR12","doi-asserted-by":"publisher","unstructured":"Yanyu Li, J.H., Wen, Y., Evangelidis, G., Salahi, K., Wang, Y., Tulyakov, S., Ren, J.: Rethinking vision transformers for mobilenet size and speed. In Proceedings of the IEEE\/CVF international conference on computer vision 16889\u201316900 (2023). https:\/\/doi.org\/10.1109\/iccv51070.2023.01549","DOI":"10.1109\/iccv51070.2023.01549"},{"key":"1906_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103910","volume":"97","author":"K Tong","year":"2020","unstructured":"Tong, K., Yiquan, W., Zhou, F.: Recent advances in small object detection based on deep learning: A review. Image Vis. Comput. 97, 103910 (2020). https:\/\/doi.org\/10.1016\/j.imavis.2020.103910","journal-title":"Image Vis. Comput."},{"key":"1906_CR14","doi-asserted-by":"publisher","unstructured":"Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3883\u20133891, (2017). https:\/\/doi.org\/10.1109\/cvpr.2017.35","DOI":"10.1109\/cvpr.2017.35"},{"key":"1906_CR15","doi-asserted-by":"publisher","unstructured":"Jie, H., Shen, L., Sun, G.: Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition 7132\u20137141 (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00745","DOI":"10.1109\/cvpr.2018.00745"},{"key":"1906_CR16","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), 3\u201319, (2018). https:\/\/doi.org\/10.7717\/peerjcs.2100\/fig-6","DOI":"10.7717\/peerjcs.2100\/fig-6"},{"key":"1906_CR17","doi-asserted-by":"publisher","unstructured":"Wang, Q., Banggu, W., Zhu, P., Li, P., Zuo, W., Qinghua, H.: Eca-net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 11534\u201311542 (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.01155","DOI":"10.1109\/cvpr42600.2020.01155"},{"issue":"4","key":"1906_CR18","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/s41095-023-0364-2","volume":"9","author":"M-H Guo","year":"2023","unstructured":"Guo, M.-H., Cheng-Ze, L., Liu, Z.-N., Cheng, M.-M., Shi-Min, H.: Visual attention network. Computational visual media 9(4), 733\u2013752 (2023). https:\/\/doi.org\/10.1007\/s41095-023-0364-2","journal-title":"Visual attention network. Computational visual media"},{"key":"1906_CR19","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234\u2013241. Springer, (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1906_CR20","doi-asserted-by":"publisher","unstructured":"Oktay, O., Schlemper, J., Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et\u00a0al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999, (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_15","DOI":"10.1007\/978-3-030-00934-2_15"},{"key":"1906_CR21","doi-asserted-by":"publisher","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 4489\u20134497, (2015). https:\/\/doi.org\/10.1109\/iccv.2015.510","DOI":"10.1109\/iccv.2015.510"},{"key":"1906_CR22","doi-asserted-by":"publisher","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 13713\u201313722, (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01350","DOI":"10.1109\/cvpr46437.2021.01350"},{"key":"1906_CR23","doi-asserted-by":"publisher","unstructured":"Zhang, Q.-L., Yang, Y.-B.: Sa-net: Shuffle attention for deep convolutional neural networks. In ICASSP 2021-2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 2235\u20132239. IEEE, (2021). https:\/\/doi.org\/10.1109\/icassp39728.2021.9414568","DOI":"10.1109\/icassp39728.2021.9414568"},{"key":"1906_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111955","volume":"297","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Sun, H.: Optimizing intrinsic representation for tracking. Knowl.-Based Syst. 297, 111955 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.111955","journal-title":"Knowl.-Based Syst."},{"key":"1906_CR25","doi-asserted-by":"publisher","unstructured":"Sun, H., Zhang, Y., Zhang, H., Qiu, X., Rudas, I.J.: Learning distance constrained transformation for video tracking in car-following. IEEE Transactions on Cybernetics, (2025). https:\/\/doi.org\/10.1109\/tcyb.2025.3574326","DOI":"10.1109\/tcyb.2025.3574326"},{"key":"1906_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107587","volume":"189","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Sun, H.: Decoding split-frequency representation for cross-scale tracking. Neural Netw. 189, 107587 (2025). https:\/\/doi.org\/10.1016\/j.neunet.2025.107587","journal-title":"Neural Netw."},{"key":"1906_CR27","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980\u20132988, (2017). https:\/\/doi.org\/10.1109\/iccv.2017.324","DOI":"10.1109\/iccv.2017.324"},{"key":"1906_CR28","unstructured":"Matthew, D., Zeiler: Adadelta: an adaptive learning rate method, (2012). arXiv:1212.5701 arXiv preprint"},{"key":"1906_CR29","unstructured":"Yang, L., Zhang, R.-Y., Li, L., Xie, X.: Simam: A simple, parameter-free attention module for convolutional neural networks. In International conference on machine learning, pages 11863\u201311874. PMLR, (2021). URL https:\/\/proceedings.mlr.press\/v139\/yang21o.html. arXiv:2103.01950"},{"key":"1906_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2023.3317483","volume":"72","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Pan, H., Wang, J., Sun, W.: Facing completely occluded short-term tracking based on correlation filters. IEEE Trans. Instrum. Meas. 72, 1\u201315 (2023). https:\/\/doi.org\/10.1109\/tim.2023.3317483","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01906-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-026-01906-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01906-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T12:16:35Z","timestamp":1782130595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-026-01906-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["1906"],"URL":"https:\/\/doi.org\/10.1007\/s11554-026-01906-z","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"12 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"112"}}