{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T05:09:04Z","timestamp":1762060144527,"version":"build-2065373602"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s11554-022-01233-z","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T16:03:00Z","timestamp":1658505780000},"page":"921-930","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Real-time digital twins end-to-end multi-branch object detection with feature level selection for healthcare"],"prefix":"10.1007","volume":"19","author":[{"given":"Xiaoqin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"1233_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3130434","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Liang, W., Li, W., Yan, K., Shimizu, S., Kevin, I., Wang, K.: Hierarchical adversarial attacks against graph neural network based iot network intrusion detection system. IEEE Internet Things J. (2021). https:\/\/doi.org\/10.1109\/JIOT.2021.3130434","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"1233_CR2","doi-asserted-by":"publisher","first-page":"10","DOI":"10.26599\/BDMA.2020.9020017","volume":"4","author":"J Mabrouki","year":"2021","unstructured":"Mabrouki, J., Azrour, M., Fattah, G., Dhiba, D., ElHajjaji, S.: Intelligent monitoring system for biogas detection based on the internet of things: Mohammedia, Morocco city landfill case. Big Data Min. Anal. 4(1), 10\u201317 (2021)","journal-title":"Big Data Min. Anal."},{"issue":"10","key":"1233_CR3","doi-asserted-by":"publisher","first-page":"4997","DOI":"10.1007\/s00521-020-05286-8","volume":"33","author":"R Hu","year":"2021","unstructured":"Hu, R., Tang, Z.-R., Song, X., Luo, J., Wu, E.Q., Chang, S.: Ensemble echo network with deep architecture for time-series modeling. Neural Comput. Appl. 33(10), 4997\u20135010 (2021)","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"1233_CR4","doi-asserted-by":"publisher","first-page":"3684","DOI":"10.1007\/s10489-020-01985-w","volume":"51","author":"Z Tang","year":"2021","unstructured":"Tang, Z., Chen, Y., Wang, Z., Hu, R., Wu, E.Q.: Non-spike timing-dependent plasticity learning mechanism for memristive neural networks. Appl. Intell. 51(6), 3684\u20133695 (2021)","journal-title":"Appl. Intell."},{"key":"1233_CR5","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.neunet.2020.10.003","volume":"133","author":"R Hu","year":"2021","unstructured":"Hu, R., Zhou, S., Tang, Z.R., Chang, S., Huang, Q., Liu, Y., Han, W., Wu, E.Q.: Dmman: a two-stage audio-visual fusion framework for sound separation and event localization. Neural Netw. 133, 229\u2013239 (2021)","journal-title":"Neural Netw."},{"issue":"7","key":"1233_CR6","doi-asserted-by":"publisher","first-page":"638","DOI":"10.3390\/life11070638","volume":"11","author":"L Liu","year":"2021","unstructured":"Liu, L., Chen, X., Petinrin, O.O., Zhang, W., Rahaman, S., Tang, Z.-R., Wong, K.-C.: Machine learning protocols in early cancer detection based on liquid biopsy: a survey. Life 11(7), 638 (2021)","journal-title":"Life"},{"key":"1233_CR7","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"issue":"1","key":"1233_CR8","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TCSS.2020.2987846","volume":"8","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Liang, W., Kevin, I., Wang, K., Yang, L.T.: Deep correlation mining based on hierarchical hybrid networks for heterogeneous big data recommendations. IEEE Trans. Comput. Soc. Syst. 8(1), 171\u2013178 (2020)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"2","key":"1233_CR9","doi-asserted-by":"publisher","first-page":"84","DOI":"10.26599\/BDMA.2020.9020012","volume":"4","author":"KK Singh","year":"2021","unstructured":"Singh, K.K., Singh, A.: Diagnosis of covid-19 from chest x-ray images using wavelets-based depthwise convolution network. Big Data Min. Anal. 4(2), 84\u201393 (2021)","journal-title":"Big Data Min. Anal."},{"key":"1233_CR10","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1233_CR11","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1233_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3116085","author":"W Liang","year":"2021","unstructured":"Liang, W., Hu, Y., Zhou, X., Pan, Y., Kevin, I., Wang, K.: Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial iot. IEEE Trans. Ind. Inform. (2021). https:\/\/doi.org\/10.1109\/TII.2021.3116085","journal-title":"IEEE Trans. Ind. Inform."},{"key":"1233_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3130028","author":"Z Tang","year":"2021","unstructured":"Tang, Z., Sun, Z.H., Wu, E.Q., Wei, C.F., Ming, D., Chen, S.: Mrcg: a mri retrieval system with convolutional and graph neural networks for secure and private iomt. IEEE J. Biomed. Health Inform. (2021). https:\/\/doi.org\/10.1109\/JBHI.2021.3130028","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1233_CR14","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fcos: fully convolutional one-stage object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9627\u20139636 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"1233_CR15","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Ren, W., Hu, W., Yu, Y.: High-level semantic feature detection: a new perspective for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5187\u20135196 (2019)","DOI":"10.1109\/CVPR.2019.00533"},{"issue":"16","key":"1233_CR16","doi-asserted-by":"publisher","first-page":"12588","DOI":"10.1109\/JIOT.2021.3077449","volume":"8","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Xu, X., Liang, W., Zeng, Z., Yan, Z.: Deep-learning-enhanced multitarget detection for end-edge-cloud surveillance in smart iot. IEEE Internet Things J. 8(16), 12588\u201312596 (2021)","journal-title":"IEEE Internet Things J."},{"key":"1233_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3077937","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Yang, X., Ma, J., Kevin, I., Wang, K.: Energy efficient smart routing based on link correlation mining for wireless edge computing in iot. IEEE Internet Things J. (2021). https:\/\/doi.org\/10.1109\/JIOT.2021.3077937","journal-title":"IEEE Internet Things J."},{"key":"1233_CR18","doi-asserted-by":"crossref","unstructured":"Shen, G., Tang, Z.R., Shen, P., Yu, Y.: Hq-trans: a high-quality screening based image translation framework for unsupervised cross-domain pedestrian detection. In: International Conference on Image and Graphics, pp. 16\u201327. Springer (2021)","DOI":"10.1007\/978-3-030-87355-4_2"},{"issue":"3","key":"1233_CR19","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1049\/cvi2.12081","volume":"16","author":"G Shen","year":"2022","unstructured":"Shen, G., Yu, Y., Tang, Z.-R., Chen, H., Zhou, Z.: Hqa-trans: an end-to-end high-quality-awareness image translation framework for unsupervised cross-domain pedestrian detection. IET Comput. Vis. 16(3), 218\u2013229 (2022)","journal-title":"IET Comput. Vis."},{"issue":"2","key":"1233_CR20","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1109\/TII.2021.3061419","volume":"18","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Xu, X., Liang, W., Zeng, Z., Shimizu, S., Yang, L.T., Jin, Q.: Intelligent small object detection for digital twin in smart manufacturing with industrial cyber-physical systems. IEEE Trans. Ind. Inf. 18(2), 1377\u20131386 (2021)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"1233_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2022.3142265","author":"X Xu","year":"2022","unstructured":"Xu, X., Tian, H., Zhang, X., Qi, L., He, Q., Dou, W.: Discov: distributed Covid-19 detection on x-ray images with edge-cloud collaboration. IEEE Trans. Serv. Comput. (2022). https:\/\/doi.org\/10.1109\/TSC.2022.3142265","journal-title":"IEEE Trans. Serv. Comput."},{"key":"1233_CR22","unstructured":"Dai, J., Li, Y., He, K., Sun, J.: R-fcn: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379\u2013387 (2016)"},{"issue":"3","key":"1233_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447032","volume":"17","author":"X Xu","year":"2021","unstructured":"Xu, X., Fang, Z., Zhang, J., He, Q., Yu, D., Qi, L., Dou, W.: Edge content caching with deep spatiotemporal residual network for iov in smart city. ACM Trans. Sens. Netw. (TOSN) 17(3), 1\u201333 (2021)","journal-title":"ACM Trans. Sens. Netw. (TOSN)"},{"key":"1233_CR24","doi-asserted-by":"crossref","unstructured":"Law, H., Deng, J.: Cornernet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734\u2013750 (2018)","DOI":"10.1007\/978-3-030-01264-9_45"},{"issue":"8","key":"1233_CR25","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1109\/TPDS.2021.3131680","volume":"33","author":"L Yuan","year":"2021","unstructured":"Yuan, L., He, Q., Chen, F., Zhang, J., Qi, L., Xu, X., Xiang, Y., Yang, Y.: Csedge: enabling collaborative edge storage for multi-access edge computing based on blockchain. IEEE Trans. Parallel Distrib. Syst. 33(8), 1873\u20131887 (2021)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"1233_CR26","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., Savvides, M.: Feature selective anchor-free module for single-shot object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 840\u2013849 (2019)","DOI":"10.1109\/CVPR.2019.00093"},{"key":"1233_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2975587","author":"L Qi","year":"2020","unstructured":"Qi, L., He, Q., Chen, F., Zhang, X., Dou, W., Ni, Q.: Data-driven web apis recommendation for building web applications. IEEE Trans. Big Data (2020). https:\/\/doi.org\/10.1109\/TBDATA.2020.2975587","journal-title":"IEEE Trans. Big Data"},{"issue":"2","key":"1233_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3401979","volume":"17","author":"X Xu","year":"2021","unstructured":"Xu, X., Fang, Z., Qi, L., Zhang, X., He, Q., Zhou, X.: Tripres: traffic flow prediction driven resource reservation for multimedia iov with edge computing. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 17(2), 1\u201321 (2021)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"1233_CR29","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1233_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3139363","author":"L Qi","year":"2021","unstructured":"Qi, L., Yang, Y., Zhou, X., Rafique, W., Ma, J.: Fast anomaly identification based on multi-aspect data streams for intelligent intrusion detection toward secure industry 4.0. IEEE Trans. Ind. Inform. (2021). https:\/\/doi.org\/10.1109\/TII.2021.3139363","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"1s","key":"1233_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3417293","volume":"17","author":"L Qi","year":"2021","unstructured":"Qi, L., Song, H., Zhang, X., Srivastava, G., Xu, X., Yu, S.: Compatibility-aware web api recommendation for mashup creation via textual description mining. ACM Trans. Multimed. Comput. Commun. Appl. 17(1s), 1\u201319 (2021)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"1233_CR32","doi-asserted-by":"crossref","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, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"9","key":"1233_CR33","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1007\/s00500-015-1943-7","volume":"21","author":"J Shi","year":"2017","unstructured":"Shi, J., Wu, J., Anisetti, M., Damiani, E., Jeon, G.: An interval type-2 fuzzy active contour model for auroral oval segmentation. Soft. Comput. 21(9), 2325\u20132345 (2017)","journal-title":"Soft. Comput."},{"key":"1233_CR34","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ins.2016.03.016","volume":"354","author":"G Jeon","year":"2016","unstructured":"Jeon, G., Anisetti, M., Wang, L., Damiani, E.: Locally estimated heterogeneity property and its fuzzy filter application for deinterlacing. Inf. Sci. 354, 112\u2013130 (2016)","journal-title":"Inf. Sci."},{"issue":"6","key":"1233_CR35","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.1109\/TII.2020.3012157","volume":"17","author":"L Qi","year":"2020","unstructured":"Qi, L., Hu, C., Zhang, X., Khosravi, M.R., Sharma, S., Pang, S., Wang, T.: Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Trans. Ind. Inf. 17(6), 4159\u20134167 (2020)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"1233_CR36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M.., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp. 740\u2013755. Springer (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1233_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133221 (2017)","DOI":"10.1109\/CVPR.2017.474"},{"key":"1233_CR38","unstructured":"Chen, Y., Zhao, F., Lu, Y., Chen, X.: Dynamic task offloading for mobile edge computing with hybrid energy supply. Tsinghua Science and Technology, vol. 10 (2021)"},{"issue":"11","key":"1233_CR39","doi-asserted-by":"publisher","first-page":"5369","DOI":"10.1109\/TIP.2016.2604489","volume":"25","author":"J Wu","year":"2016","unstructured":"Wu, J., Anisetti, M., Wu, W., Damiani, E., Jeon, G.: Bayer demosaicking with polynomial interpolation. IEEE Trans. Image Process. 25(11), 5369\u20135382 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"1233_CR40","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"issue":"5","key":"1233_CR41","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1007\/s11554-021-01144-5","volume":"18","author":"I Ahmed","year":"2021","unstructured":"Ahmed, I., Jeon, G.: A real-time person tracking system based on siammask network for intelligent video surveillance. J. Real-Time Image Proc. 18(5), 1803\u20131814 (2021)","journal-title":"J. Real-Time Image Proc."},{"key":"1233_CR42","doi-asserted-by":"crossref","unstructured":"Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K., Vasudevan, R.: Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks? (2016). arXiv:1610.01983","DOI":"10.1109\/ICRA.2017.7989092"},{"issue":"11","key":"1233_CR43","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Robot. Res. 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res."},{"key":"1233_CR44","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 304\u2013311. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206631"},{"issue":"5","key":"1233_CR45","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1007\/s11554-021-01166-z","volume":"18","author":"I Ahmed","year":"2021","unstructured":"Ahmed, I., Ahmad, M., Jeon, G.: A real-time efficient object segmentation system based on u-net using aerial drone images. J. Real-Time Image Proc. 18(5), 1745\u20131758 (2021)","journal-title":"J. Real-Time Image Proc."},{"key":"1233_CR46","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, Y., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097\u20132106 (2017)","DOI":"10.1109\/CVPR.2017.369"},{"issue":"2","key":"1233_CR47","doi-asserted-by":"publisher","first-page":"270","DOI":"10.26599\/TST.2020.9010025","volume":"27","author":"X Xu","year":"2021","unstructured":"Xu, X., Li, H., Xu, W., Liu, Z., Yao, L., Dai, F.: Artificial intelligence for edge service optimization in internet of vehicles: a survey. Tsinghua Sci. Technol. 27(2), 270\u2013287 (2021)","journal-title":"Tsinghua Sci. Technol."},{"key":"1233_CR48","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1233_CR49","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Hu, W., Liang, X., Chen, X.: Learning efficient single-stage pedestrian detectors by asymptotic localization fitting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 618\u2013634 (2018)","DOI":"10.1007\/978-3-030-01264-9_38"},{"key":"1233_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: How far are we from solving pedestrian detection? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1259\u20131267 (2016)","DOI":"10.1109\/CVPR.2016.141"},{"key":"1233_CR51","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., VanGool, L.: Domain adaptive faster r-cnn for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3339\u20133348 (2018)","DOI":"10.1109\/CVPR.2018.00352"},{"key":"1233_CR52","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). arXiv:1804.02767"},{"key":"1233_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637\u2013653 (2018)","DOI":"10.1007\/978-3-030-01219-9_39"},{"key":"1233_CR54","doi-asserted-by":"crossref","unstructured":"Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., Shen, C.: Repulsion loss: detecting pedestrians in a crowd. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7774\u20137783 (2018)","DOI":"10.1109\/CVPR.2018.00811"},{"key":"1233_CR55","doi-asserted-by":"crossref","unstructured":"Song, T., Sun, L., Xie, D., Sun, H., Pu, S.: Small-scale pedestrian detection based on topological line localization and temporal feature aggregation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 536\u2013551 (2018)","DOI":"10.1007\/978-3-030-01234-2_33"},{"key":"1233_CR56","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"issue":"8","key":"1233_CR57","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r, P., Appel, R., Belongie, R., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532\u20131545 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-022-01233-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-022-01233-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-022-01233-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T12:13:13Z","timestamp":1663157593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-022-01233-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,22]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["1233"],"URL":"https:\/\/doi.org\/10.1007\/s11554-022-01233-z","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2022,7,22]]},"assertion":[{"value":"9 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2022","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"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human participants"}},{"value":"This article does not contain any studies with animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Animals studies"}},{"value":"This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}