{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T22:54:24Z","timestamp":1774220064379,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science and Technology Research Projects of Education Department of Liaoning Province of China","award":["No. LJ212510144002"],"award-info":[{"award-number":["No. LJ212510144002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s11760-026-05159-y","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:19:01Z","timestamp":1771287541000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved vehicle multi-target tracking in complex road scenes based on CenterTrack"],"prefix":"10.1007","volume":"20","author":[{"given":"Hao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongfei","family":"Huai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"issue":"21","key":"5159_CR1","doi-asserted-by":"publisher","first-page":"10741","DOI":"10.3390\/app122110741","volume":"12","author":"S Guo","year":"2022","unstructured":"Guo, S., Wang, S., Yang, Z., Wang, L., Zhang, H., Guo, P., Gao, Y., Guo, J.: A review of deep learning-based visual multi-object tracking algorithms for autonomous driving. Appl. Sci. 12(21), 10741 (2022)","journal-title":"Appl. Sci."},{"key":"5159_CR2","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)"},{"issue":"1","key":"5159_CR3","first-page":"104","volume":"43","author":"S Sun","year":"2021","unstructured":"Sun, S., Akhtar, N., Song, H., Mian, A., Shah, M.: Deep affinity network for multiple object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 104\u2013119 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5159_CR4","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"5159_CR5","doi-asserted-by":"crossref","unstructured":"Zhou, X., Koltun, V., Kr\u00e4henb\u00fchl, P.: Tracking objects as points. In: Proceedings of the European Conference on Computer Vision (ECCV) pp. 474\u2013490 (2020)","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"5159_CR6","doi-asserted-by":"publisher","first-page":"130060","DOI":"10.1109\/ACCESS.2023.3327262","volume":"11","author":"X Hu","year":"2023","unstructured":"Hu, X., Jeon, Y., Gwak, J.: Fftransmot: feature-fused transformer for enhanced multi-object tracking. IEEE Access 11, 130060\u2013130071 (2023)","journal-title":"IEEE Access"},{"key":"5159_CR7","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"5159_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5159_CR9","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2403\u20132412 (2018)","DOI":"10.1109\/CVPR.2018.00255"},{"key":"5159_CR10","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"5159_CR11","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"5159_CR12","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Adam, H., Le, Q.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"5159_CR13","doi-asserted-by":"crossref","unstructured":"Gomaa, A.: Advanced domain adaptation technique for object detection leveraging semi-automated dataset construction and enhanced yolov8. In: 2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES) pp. 211\u2013214 (2024). IEEE","DOI":"10.1109\/NILES63360.2024.10753164"},{"issue":"6","key":"5159_CR14","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/wevj15060255","volume":"15","author":"A Gomaa","year":"2024","unstructured":"Gomaa, A., Abdalrazik, A.: Novel deep learning domain adaptation approach for object detection using semi-self building dataset and modified yolov4. World Electr. Veh. J. 15(6), 255 (2024)","journal-title":"World Electr. Veh. J."},{"key":"5159_CR15","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (ICML) pp. 6105\u20136114 (2019)"},{"key":"5159_CR16","unstructured":"Tan, M., Le, Q.: Efficientnetv2: Smaller models and faster training. arXiv preprint arXiv:2104.00298 (2021)"},{"key":"5159_CR17","doi-asserted-by":"publisher","first-page":"33837","DOI":"10.1007\/s11042-024-20546-8","volume":"84","author":"A Gomaa","year":"2025","unstructured":"Gomaa, A., Saad, O.M.: Residual channel-attention (RCA) network for remote sensing image scene classification. Multimedia Tools Appl. 84, 33837\u201333861 (2025)","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"5159_CR18","doi-asserted-by":"publisher","first-page":"17493","DOI":"10.1038\/s41598-025-02111-x","volume":"15","author":"OF Hassan","year":"2025","unstructured":"Hassan, O.F., Ibrahim, A.F., Gomaa, A., Makhlouf, M.A., Hafiz, B.: Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach. Sci. Rep. 15(1), 17493 (2025)","journal-title":"Sci. Rep."},{"key":"5159_CR19","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP) pp. 3464\u20133468 (2016). IEEE","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"5159_CR20","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP) pp. 3645\u20133649 (2017). IEEE","DOI":"10.1109\/ICIP.2017.8296962"},{"issue":"18","key":"5159_CR21","doi-asserted-by":"publisher","first-page":"25443","DOI":"10.1007\/s11042-022-12370-9","volume":"81","author":"A Gomaa","year":"2022","unstructured":"Gomaa, A., Minematsu, T., Abdelwahab, M.M., Abo-Zahhad, M., Taniguchi, R.: Faster cnn-based vehicle detection and counting strategy for fixed camera scenes. Multimedia Tools Appl. 81(18), 25443\u201325471 (2022)","journal-title":"Multimedia Tools Appl."},{"issue":"20","key":"5159_CR22","doi-asserted-by":"publisher","first-page":"4588","DOI":"10.3390\/s19204588","volume":"19","author":"A Gomaa","year":"2019","unstructured":"Gomaa, A., Abdelwahab, M.M., Abo-Zahhad, M., Minematsu, T., Taniguchi, R.: Robust vehicle detection and counting algorithm employing a convolution neural network and optical flow. Sensors 19(20), 4588 (2019)","journal-title":"Sensors"},{"key":"5159_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., Wang, X.: Bytetrack: Multi-object tracking by associating every detection box. In: Proceedings of the European Conference on Computer Vision (ECCV) pp. 1\u201321 (2022)","DOI":"10.1007\/978-3-031-20047-2_1"},{"issue":"35","key":"5159_CR24","doi-asserted-by":"publisher","first-page":"26023","DOI":"10.1007\/s11042-020-09242-5","volume":"79","author":"A Gomaa","year":"2020","unstructured":"Gomaa, A., Abdelwahab, M.M., Abo-Zahhad, M.: Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis. Multimedia Tools Appl. 79(35), 26023\u201326043 (2020)","journal-title":"Multimedia Tools Appl."},{"key":"5159_CR25","doi-asserted-by":"crossref","unstructured":"Gomaa, A., Abdelwahab, M.M., Abo-Zahhad, M.: Real-time algorithm for simultaneous vehicle detection and tracking in aerial view videos. In: 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) pp. 222\u2013225 (2018). IEEE","DOI":"10.1109\/MWSCAS.2018.8624022"},{"issue":"11","key":"5159_CR26","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: Fairmot: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vis. 129(11), 3069\u20133087 (2021)","journal-title":"Int. J. Comput. Vis."},{"key":"5159_CR27","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Meinhardt, T., Leal-Taixe, L.: Tracking without bells and whistles. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) pp. 941\u2013951 (2019)","DOI":"10.1109\/ICCV.2019.00103"},{"issue":"6","key":"5159_CR28","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5159_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, L., Liu, Y., Li, Y., Wang, S.: Towards real-time multi-object tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 107\u2013122 (2020)","DOI":"10.1007\/978-3-030-58621-8_7"},{"key":"5159_CR30","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"5159_CR31","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/TIP.2025.3526066","volume":"34","author":"X Cao","year":"2025","unstructured":"Cao, X., Zheng, Y., Yao, Y., Qin, H., Cao, X., Guo, S.: Topic: a parallel association paradigm for multi-object tracking under complex motions and diverse scenes. IEEE Trans. Image Process. 34, 743\u2013758 (2025)","journal-title":"IEEE Trans. Image Process."},{"key":"5159_CR32","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"5159_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"5159_CR34","unstructured":"Sun, P., Cao, J., Jiang, Y., Zhang, R., Xie, E., Yuan, Z., Wang, C., Luo, P.: Transtrack: Multiple object tracking with transformer. arXiv preprint arXiv:2012.15460 (2020)"},{"issue":"10","key":"5159_CR35","doi-asserted-by":"publisher","first-page":"9866","DOI":"10.1109\/TCSVT.2024.3404275","volume":"34","author":"J Lin","year":"2024","unstructured":"Lin, J., Liang, G., Zhang, R.: Lttrack: rethinking the tracking framework for long-term multi-object tracking. IEEE Trans. Circuits Syst. Video Technol. 34(10), 9866\u20139881 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5159_CR36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"5159_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 11531\u201311539 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"5159_CR38","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 3354\u20133361 (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"5159_CR39","doi-asserted-by":"publisher","first-page":"102907","DOI":"10.1016\/j.cviu.2020.102907","volume":"193","author":"L Wen","year":"2020","unstructured":"Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M.-C., Qi, H., Lim, J., Yang, M.-H., Lyu, S.: Ua-detrac: a new benchmark and protocol for multi-object detection and tracking. Comput. Vis. Image Underst. 193, 102907 (2020)","journal-title":"Comput. Vis. Image Underst."},{"issue":"1","key":"5159_CR40","first-page":"246309","volume":"2008","author":"K Bernardin","year":"2008","unstructured":"Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 2008(1), 246309 (2008)","journal-title":"EURASIP J. Image Video Process."},{"key":"5159_CR41","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Proceedings of the European Conference on Computer Vision (ECCV) pp. 17\u201335 (2016)","DOI":"10.1007\/978-3-319-48881-3_2"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05159-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05159-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05159-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T22:16:01Z","timestamp":1774217761000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05159-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,17]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["5159"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05159-y","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,17]]},"assertion":[{"value":"27 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2026","order":4,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"105"}}