{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:24:14Z","timestamp":1762917854886,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2018,4,25]],"date-time":"2018-04-25T00:00:00Z","timestamp":1524614400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61472063"],"award-info":[{"award-number":["No.61472063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s11063-018-9837-1","type":"journal-article","created":{"date-parts":[[2018,4,25]],"date-time":"2018-04-25T11:11:17Z","timestamp":1524654677000},"page":"923-937","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Rapid Pedestrian Detection Based on Deep Omega-Shape Features with Partial Occlusion Handing"],"prefix":"10.1007","volume":"49","author":[{"given":"Yuting","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,4,25]]},"reference":[{"key":"9837_CR1","unstructured":"Dai J, Li, Y, He, K, Sun J (2016) R-FCN: object Detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379\u2013387"},{"issue":"2","key":"9837_CR2","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M et al (2010) The PASCAL visual object classes (VOC) challenge. IJCV 88(2):303\u2013338","journal-title":"IJCV"},{"key":"9837_CR3","doi-asserted-by":"crossref","unstructured":"Liu W et al (2016) SSD: single Shot multibox detector. In: ECCV, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"9837_CR4","unstructured":"Redmon J, Farhadi A (2016) YOLO9000: better, faster, stronger. arXiv preprint \n                    arXiv:1612.08242"},{"issue":"4","key":"9837_CR5","first-page":"734","volume":"34","author":"D Piotr","year":"2012","unstructured":"Piotr D et al (2012) Pedestrian detection: an evaluation of the state of the art. TPAMI 34(4):734\u2013761","journal-title":"TPAMI"},{"key":"9837_CR6","doi-asserted-by":"crossref","unstructured":"Li M et al (2009) Rapid and robust human detection and tracking based on omega-shape features. In: IEEE international conference on image processing, pp 2545\u20132548","DOI":"10.1109\/ICIP.2009.5414008"},{"key":"9837_CR7","doi-asserted-by":"crossref","unstructured":"Li M et al (2008) Estimating the number of people in crowded scenes by mid-based foreground segmentation and head-shoulder detection. In: International conference on pattern recognition, pp 1\u20134","DOI":"10.1109\/ICPR.2008.4761705"},{"key":"9837_CR8","doi-asserted-by":"crossref","unstructured":"Shen F et al (2018) Unsupervised deep hashing with similarity-adaptive and discrete optimization. In: IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2018.2789887"},{"issue":"12","key":"9837_CR9","doi-asserted-by":"publisher","first-page":"5610","DOI":"10.1109\/TIP.2016.2612883","volume":"25","author":"F Shen","year":"2016","unstructured":"Shen F et al (2016) A fast optimization method for general binary code learning. IEEE Trans Image Process 25(12):5610\u20135621","journal-title":"IEEE Trans Image Process"},{"key":"9837_CR10","unstructured":"Liliang Z, Liang L, Xiaodan L, Kaiming H (2016) Is faster R-CNN doing well for pedestrian detection?. In: ECCV, pp 443\u2013457"},{"key":"9837_CR11","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal network. In: NIPS, pp 91\u201399"},{"key":"9837_CR12","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neucom.2012.08.007","volume":"101","author":"F Shen","year":"2013","unstructured":"Shen F, Tang Z, Jingsong X (2013) Locality constrained representation based classification with spatial pyramid patches. Neurocomputing 101:104\u2013115","journal-title":"Neurocomputing"},{"key":"9837_CR13","doi-asserted-by":"crossref","unstructured":"Huang J et al (2017) Speed\/accuracy trade-offs for modern convolutional object detectors. In: CVPR","DOI":"10.1109\/CVPR.2017.351"},{"key":"9837_CR14","doi-asserted-by":"crossref","unstructured":"Dollar P et al (2009) Integral channel features. In: British machine vision conference","DOI":"10.5244\/C.23.91"},{"key":"9837_CR15","doi-asserted-by":"crossref","unstructured":"Tian Y, Luo P, Wang X, Tang X (2015) Pedestrian detection aided by deep learning semantic tasks. In: CVPR, pp 5079\u20135087","DOI":"10.1109\/CVPR.2015.7299143"},{"issue":"8","key":"9837_CR16","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"D Piotr","year":"2014","unstructured":"Piotr D et al (2014) Fast feature pyramids for object detection. PAMI 36(8):1532\u20131545","journal-title":"PAMI"},{"key":"9837_CR17","doi-asserted-by":"crossref","unstructured":"Zhang S, Benenson R, Schiele B (2015) Filtered channel features for pedestrian detection. In: CVPR","DOI":"10.1109\/CVPR.2015.7298784"},{"key":"9837_CR18","first-page":"886","volume":"1","author":"N Dalal","year":"2005","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. CVPR 1:886\u2013893","journal-title":"CVPR"},{"issue":"9","key":"9837_CR19","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF et al (2010) Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627\u20131645","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9837_CR20","doi-asserted-by":"crossref","unstructured":"Hosang J, Omran M, Benenson R, Schiele B (2015) Taking a deeper look at pedestrians. In: CVPR, pp 4073\u20134082","DOI":"10.1109\/CVPR.2015.7299034"},{"key":"9837_CR21","doi-asserted-by":"crossref","unstructured":"Cai Z, Saberian M, Vasconcelos N (2015) Learning complexity-aware cascades for deep pedestrian detection. In: ICCV, pp 3361\u20133369","DOI":"10.1109\/ICCV.2015.384"},{"key":"9837_CR22","doi-asserted-by":"crossref","unstructured":"Wang X, Shrivastava A, Gupta A (2017) A-fast-RCNN: hard positive genneration via adversary for object detection. arXiv preprint \n                    arXiv:1704.03414","DOI":"10.1109\/CVPR.2017.324"},{"key":"9837_CR23","unstructured":"Goodfellow I et al (2014) Generative adversarial nets. In: NIPS, pp 2672\u20132680"},{"key":"9837_CR24","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. arXiv preprint \n                    arXiv:1504.08083","DOI":"10.1109\/ICCV.2015.169"},{"key":"9837_CR25","doi-asserted-by":"crossref","unstructured":"Zhang S, Benenson R, Schiele B (2017) CityPersons: a diverse dataset for pedestrian detection. arXiv preprint \n                    arXiv:1702.05693","DOI":"10.1109\/CVPR.2017.474"},{"key":"9837_CR26","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: CVPR, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9837_CR27","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9837_CR28","doi-asserted-by":"crossref","unstructured":"Stewart R, Andriluka M, Ng AY (2016) End-to-end people detection in crowded scenes. In: CVPR, pp 2325\u20132333","DOI":"10.1109\/CVPR.2016.255"},{"key":"9837_CR29","doi-asserted-by":"crossref","unstructured":"Liu P, Zhou X, Cai S (2016) Omega-shape feature learning for robust human detection. In: CCPR, pp 290\u2013303","DOI":"10.1007\/978-981-10-3002-4_25"},{"key":"9837_CR30","doi-asserted-by":"crossref","unstructured":"Lenc K, Vedaldi A (2015) R-CNN minus R. arXiv preprint \n                    arXiv:1506.06981","DOI":"10.5244\/C.29.5"},{"key":"9837_CR31","doi-asserted-by":"crossref","unstructured":"Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. In: CVPR, pp 761\u2013769","DOI":"10.1109\/CVPR.2016.89"},{"key":"9837_CR32","doi-asserted-by":"crossref","unstructured":"Zitnick CL, Dollar P (2014) Edge boxes: Locating object proposals from edges? In: ECCV, pp 391\u2013405","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"9837_CR33","doi-asserted-by":"crossref","unstructured":"Bodla N et al (2017) Soft-NMS\u2014improving object detection with one line of code. In: ICCV, pp 5561\u20135569","DOI":"10.1109\/ICCV.2017.593"},{"key":"9837_CR34","doi-asserted-by":"crossref","unstructured":"Ristani E et al (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: ECCV, pp 17\u201335","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"9837_CR35","doi-asserted-by":"crossref","unstructured":"Jia Y et al (2014) Caffe: convolutional architecture for fast feature embedding. In: ACM, pp 675\u2013678","DOI":"10.1145\/2647868.2654889"},{"key":"9837_CR36","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: CVPR, pp 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"9837_CR37","doi-asserted-by":"crossref","unstructured":"Lin T-Y et al (2017) Feature pyramid networks for object detection. In: CVPR","DOI":"10.1109\/CVPR.2017.106"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-018-9837-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-018-9837-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-018-9837-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T08:08:23Z","timestamp":1559635703000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-018-9837-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,25]]},"references-count":37,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["9837"],"URL":"https:\/\/doi.org\/10.1007\/s11063-018-9837-1","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2018,4,25]]},"assertion":[{"value":"25 April 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}