{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T23:28:26Z","timestamp":1779319706239,"version":"3.51.4"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319541839","type":"print"},{"value":"9783319541846","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-54184-6_26","type":"book-chapter","created":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T10:44:25Z","timestamp":1489056265000},"page":"416-430","source":"Crossref","is-referenced-by-count":7,"title":["Scale-Adaptive Deconvolutional Regression Network for Pedestrian Detection"],"prefix":"10.1007","author":[{"given":"Yousong","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinqiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanqing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,10]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: How far are we from solving pedestrian detection? In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.141"},{"key":"26_CR2","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, CVPR 2005, vol. 1, pp. 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features (2009)","DOI":"10.5244\/C.23.91"},{"key":"26_CR4","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1532\u20131545 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, S., Bauckhage, C., Cremers, A.: Informed haar-like features improve pedestrian detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 947\u2013954 (2014)","DOI":"10.1109\/CVPR.2014.126"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1751\u20131760. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298784"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"26_CR8","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627\u20131645 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Cascade object detection with deformable part models. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2241\u20132248. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539906"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626\u20133633 (2013)","DOI":"10.1109\/CVPR.2013.465"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Wang, X.: A discriminative deep model for pedestrian detection with occlusion handling. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3258\u20133265. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248062"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Wang, X.: Joint deep learning for pedestrian detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2056\u20132063 (2013)","DOI":"10.1109\/ICCV.2013.257"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"26_CR14","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"26_CR16","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)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"26_CR19","unstructured":"Badrinarayanan, V., Handa, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling (2015). arXiv preprint arXiv:1505.07293"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks (2015)","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"26_CR21","unstructured":"Bell, S., Zitnick, C.L., Bala, K., Girshick, R.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks (2015). arXiv preprint arXiv:1512.04143"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 447\u2013456 (2015)","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"26_CR23","unstructured":"Zagoruyko, S., Lerer, A., Lin, T.Y., Pinheiro, P.O., Gross, S., Chintala, S., Doll\u00e1r, P.: A multipath network for object detection (2016). arXiv preprint arXiv:1604.02135"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Cai, Z., Saberian, M., Vasconcelos, N.: Learning complexity-aware cascades for deep pedestrian detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3361\u20133369 (2015)","DOI":"10.1109\/ICCV.2015.384"},{"key":"26_CR25","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104, 154\u2013171 (2013)","journal-title":"Int. J. Comput. Vis."},{"key":"26_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-10602-1_26","volume-title":"Computer Vision \u2013 ECCV 2014","author":"CL Zitnick","year":"2014","unstructured":"Zitnick, C.L., Doll\u00e1r, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391\u2013405. Springer, Heidelberg (2014). doi: 10.1007\/978-3-319-10602-1_26"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Arbel\u00e1ez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 328\u2013335 (2014)","DOI":"10.1109\/CVPR.2014.49"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., Omran, M., Schiele, B.: Taking a deeper look at pedestrians. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299034"},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Tian, Y., Luo, P., Wang, X., Tang, X.: Pedestrian detection aided by deep learning semantic tasks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5079\u20135087 (2015)","DOI":"10.1109\/CVPR.2015.7299143"},{"key":"26_CR30","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Dollar","year":"2012","unstructured":"Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743\u2013761 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"26_CR32","unstructured":"Li, J., Liang, X., Shen, S., Xu, T., Yan, S.: Scale-aware fast R-CNN for pedestrian detection (2015). arXiv preprint arXiv:1510.08160"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Yang, F., Choi, W., Lin, Y.: Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.234"},{"key":"26_CR34","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556"},{"key":"26_CR35","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). arXiv preprint arXiv:1512.03385"},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Object detection via a multi-region and semantic segmentation-aware CNN model. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1134\u20131142 (2015)","DOI":"10.1109\/ICCV.2015.135"},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: Proceedings of IEEE International Conference on Computer Vision, pp. 82\u201390 (2015)","DOI":"10.1109\/ICCV.2015.18"},{"key":"26_CR38","unstructured":"Nam, W., Doll\u00e1r, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: Advances in Neural Information Processing Systems, pp. 424\u2013432 (2014)"},{"key":"26_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-319-16181-5_47","volume-title":"Computer Vision - ECCV 2014 Workshops","author":"R Benenson","year":"2015","unstructured":"Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 613\u2013627. Springer, Heidelberg (2015). doi: 10.1007\/978-3-319-16181-5_47"},{"key":"26_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1007\/978-3-319-10593-2_36","volume-title":"Computer Vision \u2013 ECCV 2014","author":"S Paisitkriangkrai","year":"2014","unstructured":"Paisitkriangkrai, S., Shen, C., Hengel, A.: Strengthening the effectiveness of pedestrian detection with spatially pooled features. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 546\u2013561. Springer, Heidelberg (2014). doi: 10.1007\/978-3-319-10593-2_36"},{"key":"26_CR41","unstructured":"Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., Urtasun, R.: 3D object proposals for accurate object class detection. In: NIPS (2015)"},{"key":"26_CR42","doi-asserted-by":"crossref","unstructured":"Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: Monocular 3D object detection for autonomous driving. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.236"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2016"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-54184-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T10:43:10Z","timestamp":1568889790000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-54184-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319541839","9783319541846"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-54184-6_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}