{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T05:53:22Z","timestamp":1715320402218},"reference-count":24,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2021,10,1]]},"DOI":"10.1587\/transfun.2020eal2110","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T22:17:30Z","timestamp":1616710650000},"page":"1440-1444","source":"Crossref","is-referenced-by-count":1,"title":["Co-Head Pedestrian Detection in Crowded Scenes"],"prefix":"10.1587","volume":"E104.A","author":[{"given":"Chen","family":"CHEN","sequence":"first","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maojun","family":"ZHANG","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanlin","family":"TAN","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaxin","family":"XIAO","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] Z. Yang, J. Li, and H. Li, \u201cReal-time pedestrian detection for autonomous driving,\u201d International Conference on Intelligent Autonomous Systems, pp.9-13, 2018. 10.1109\/icoias.2018.8494031","DOI":"10.1109\/ICoIAS.2018.8494031"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] A. Geiger, P. Lenz, and R. Urtasun, \u201cAre we ready for autonomous driving? the kitti vision benchmark suite,\u201d Computer Vision and Pattern Recognition, pp.3354-3361, 2012. 10.1109\/cvpr.2012.6248074","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] C. Usher and W. Daley, \u201cDevelopment of a portable bicycle\/pedestrian monitoring system for safety enhancement,\u201d Electronic Imaging, vol.9407, 2015. 10.1117\/12.2079387","DOI":"10.1117\/12.2079387"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] T. Liu and T. Stathaki, \u201cFaster R-CNN for robust pedestrian detection using semantic segmentation network,\u201d Front. Neurorobot., vol.12, p.64, 2018. 10.3389\/fnbot.2018.00064","DOI":"10.3389\/fnbot.2018.00064"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A.C. Berg, \u201cSSD: Single shot multibox detector,\u201d European Conference on Computer Vision, pp.21-37, 2016. 10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Mao, T. Xiao, Y. Jiang, and Z. Cao, \u201cWhat can help pedestrian detection,\u201d Computer Vision and Pattern Recognition, pp.6034-6043, 2017. 10.1109\/cvpr.2017.639","DOI":"10.1109\/CVPR.2017.639"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] X. Wang, T. Xiao, Y. Jiang, S. Shao, J. Sun, and C. Shen, \u201cRepulsion loss: Detecting pedestrians in a crowd,\u201d Computer Vision and Pattern Recognition, pp.7774-7783, 2018. 10.1109\/cvpr.2018.00811","DOI":"10.1109\/CVPR.2018.00811"},{"key":"8","unstructured":"[8] X. Zhou, D. Wang, and P. Krahenbuhl, \u201cObjects as points,\u201d arXiv: Computer Vision and Pattern Recognition, 2019."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] F. Yu, D. Wang, E. Shelhamer, and T. Darrell, \u201cDeep layer aggregation,\u201d Computer Vision and Pattern Recognition, pp.2403-2412, 2018. 10.1109\/cvpr.2018.00255","DOI":"10.1109\/CVPR.2018.00255"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] X. Zhou, J. Zhuo, and P. Krahenbuhl, \u201cBottom-up object detection by grouping extreme and center points,\u201d 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 10.1109\/cvpr.2019.00094","DOI":"10.1109\/CVPR.2019.00094"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] H. Law and J. Deng, \u201cCornerNet: Detecting objects as paired keypoints,\u201d European Conference on Computer Vision, 2018. 10.1007\/978-3-030-01264-9_45","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, \u201cCenterNet: Keypoint triplets for object detection,\u201d arXiv: Computer Vision and Pattern Recognition, 2019.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"13","unstructured":"[13] H.Z. Newell, A. and J. Deng, \u201cAssociative embedding: End-to-end learning for joint detection and grouping,\u201d 2017."},{"key":"14","unstructured":"[14] A. Lerer, L. Wu, J. Shen, T. Lacroix, L. Wehrstedt, A. Bose, and A. Peysakhovich, \u201cPytorch-biggraph: A large-scale graph embedding system,\u201d arXiv: Learning, 2019."},{"key":"15","unstructured":"[15] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d International Conference on Learning Representations, 2015."},{"key":"16","unstructured":"[16] S. Ioffe and C. Szegedy, \u201cBatch normalization: Accelerating deep network training by reducing internal covariate shift,\u201d ICML&apos;15, pp.448-456, 2015."},{"key":"17","unstructured":"[17] P. Luo, J. Ren, Z. Peng, R. Zhang, and J. Li, \u201cDifferentiable learning-to-normalize via switchable normalization,\u201d arXiv: Computer Vision and Pattern Recognition, 2018."},{"key":"18","unstructured":"[18] S. Shao, Z. Zhao, B. Li, T. Xiao, G. Yu, X. Zhang, and J. Sun, \u201cCrowdHuman: A benchmark for detecting human in a crowd,\u201d arXiv preprint arXiv:1805.00123, 2018."},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] S. Zhang, R. Benenson, and B. Schiele, \u201cCityPersons: A diverse dataset for pedestrian detection,\u201d Computer Vision and Pattern Recognition, pp.4457-4465, 2017. 10.1109\/cvpr.2017.474","DOI":"10.1109\/CVPR.2017.474"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] T. Song, L. Sun, D. Xie, H. Sun, and S. Pu, \u201cSmall-scale pedestrian detection based on topological line localization and temporal feature aggregation,\u201d ECCV, pp.554-569, 2018. 10.1007\/978-3-030-01234-2_33","DOI":"10.1007\/978-3-030-01234-2_33"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] S. Zhang, J. Yang, and B. Schiele, \u201cOccluded pedestrian detection through guided attention in cnns,\u201d CVPR, pp.6995-7003, 2018. 10.1109\/cvpr.2018.00731","DOI":"10.1109\/CVPR.2018.00731"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] S. Liu, D. Huang, and Y. Wang, \u201cAdaptive NMS: Refining pedestrian detection in a crowd,\u201d Computer Vision and Pattern Recognition, pp.6459-6468, 2019. 10.1109\/cvpr.2019.00662","DOI":"10.1109\/CVPR.2019.00662"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] W. Liu, S. Liao, W. Ren, W. Hu, and Y. Yu, \u201cHigh-level semantic feature detection: A new perspective for pedestrian detection,\u201d CVPR, pp.5187-5196, 2019. 10.1109\/cvpr.2019.00533","DOI":"10.1109\/CVPR.2019.00533"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] C. Chi, S. Zhang, J. Xing, Z. Lei, S. Li, and X. Zou, \u201cRelational learning for joint head and human detection,\u201d AAAI, pp.10647-10654, 2020. 10.1609\/aaai.v34i07.6691","DOI":"10.1609\/aaai.v34i07.6691"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E104.A\/10\/E104.A_2020EAL2110\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T03:37:11Z","timestamp":1633145831000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E104.A\/10\/E104.A_2020EAL2110\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,1]]},"references-count":24,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2020eal2110","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,1]]},"article-number":"2020EAL2110"}}