{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:33:48Z","timestamp":1775266428111,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030026974","type":"print"},{"value":"9783030026981","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-02698-1_28","type":"book-chapter","created":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T11:46:59Z","timestamp":1541677619000},"page":"325-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Channel-Cascading Pedestrian Detection Network for Small-Size Pedestrians"],"prefix":"10.1007","author":[{"given":"Jiaojiao","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuozhong","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongjie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangjian","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengbin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,9]]},"reference":[{"key":"28_CR1","unstructured":"Zhang, Q.: Research on pedestrian detection methods on still images. University of Science and Technology of China (2015). (In Chinese)"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Feature pyramid networks for object detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 936\u2013944 (2016)","DOI":"10.1109\/CVPR.2017.106"},{"key":"28_CR3","unstructured":"Wang, B.: Pedestrian Detection Based on Deep Learning. Beijing Jiaotong University (2015). (In Chinese)"},{"key":"28_CR4","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"2","key":"28_CR5","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"David G. Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 91\u2013110 (2004)","journal-title":"International Journal of Computer Vision"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886\u2013893 (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Zhu, Q., et al.: Fast human detection using a cascade of histograms of oriented gradients. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1491\u20131498 (2006)","DOI":"10.1109\/CVPR.2006.119"},{"issue":"2","key":"28_CR8","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/asmb.537","volume":"21","author":"Pai-Hsuen Chen","year":"2005","unstructured":"Chen, P.H., Lin, C.J.: A Tutorial on -support vector machines. In: Applied Stochastic Models in Business & Industry, vol. 21, No. 2, pp. 111\u2013136 (2005)","journal-title":"Applied Stochastic Models in Business and Industry"},{"issue":"9","key":"28_CR9","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"P F Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2010)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Wang, X.: An HOG-LBP human detector with partial occlusion handling. In: Proceedings of IEEE International Conference on Computer Vision, September, Kyoto, Japan, pp. 32\u201339 (2009)","DOI":"10.1109\/ICCV.2009.5459207"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Kuo, W., Hariharan, B., Malik, J.: DeepBox: learning objectness with convolutional networks. In: IEEE International Conference on Computer Vision, pp. 2479\u20132487 (2015)","DOI":"10.1109\/ICCV.2015.285"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"9","key":"28_CR13","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"Kaiming He","year":"2015","unstructured":"He, K., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. Computer Science, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"28_CR15","unstructured":"Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems, pp. 91\u20139 (2015)"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 6517\u20136525 (2016)","DOI":"10.1109\/CVPR.2017.690"},{"key":"28_CR17","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018)"},{"key":"28_CR18","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Wei Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 21\u201337 (2016)"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Kong, T., et al.: HyperNet: towards accurate region proposal generation and joint object detection. In: Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.98"},{"key":"28_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-319-46493-0_22","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Z Cai","year":"2016","unstructured":"Cai, Z., Fan, Q., Feris, Rogerio, S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 354\u2013370. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_22"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Hariharan, B., et al.: Hypercolumns for object segmentation and fine-grained localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 447\u2013456 (2014)","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Sermanet, P., Kavukcuoglu, K., Chintala, S., et al.: Pedestrian detection with unsupervised multi-stage feature learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626\u20133633 (2013)","DOI":"10.1109\/CVPR.2013.465"},{"key":"28_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Computer Science. pp. 730\u2013734 (2014)"}],"container-title":["Lecture Notes in Computer Science","Intelligence Science and Big Data Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-02698-1_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:29:53Z","timestamp":1775262593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-02698-1_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030026974","9783030026981"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-02698-1_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"9 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IScIDE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Science and Big Data Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lanzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iscide2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iscide.lzu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"121","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}