{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:32:07Z","timestamp":1760239927439,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T00:00:00Z","timestamp":1550102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["4174095"],"award-info":[{"award-number":["4174095"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703039"],"award-info":[{"award-number":["61703039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["FRF-TP-16-047A1"],"award-info":[{"award-number":["FRF-TP-16-047A1"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages. To address this problem, we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm, which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process, and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets. The effectiveness of the proposed approach is evaluated on the Caltech pedestrian benchmark and KAIST (Korea Advanced Institute of Science and Technology) multispectral pedestrian benchmark, and validated by its promising performance on different resolution-specific test sets of both benchmarks.<\/jats:p>","DOI":"10.3390\/s19040780","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T11:54:13Z","timestamp":1550145253000},"page":"780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features"],"prefix":"10.3390","volume":"19","author":[{"given":"Chao","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu-Cheng","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1145\/2522968.2522978","article-title":"Object class detection: A survey","volume":"46","author":"Zhang","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","article-title":"Pedestrian Detection: An Evaluation of the State of the Art","volume":"34","author":"Wojek","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"Viola, P.A., and Jones, M.J. (2001, January 3\u20138). Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade. Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic (NIPS), Vancouver, BC, Canada.","key":"ref_3"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","article-title":"Cost-sensitive boosting for classification of imbalanced data","volume":"40","author":"Sun","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TPAMI.2010.71","article-title":"Cost-Sensitive Boosting","volume":"33","author":"Vasconcelos","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"Nam, W., Doll\u00e1r, P., and Han, J.H. (2014, January 8\u201313). Local Decorrelation For Improved Pedestrian Detection. Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada.","key":"ref_6"},{"doi-asserted-by":"crossref","unstructured":"Yang, B., Yan, J., Lei, Z., and Li, S.Z. (2015, January 7\u201313). Convolutional Channel Features. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","key":"ref_7","DOI":"10.1109\/ICCV.2015.18"},{"doi-asserted-by":"crossref","unstructured":"Zhu, C., and Peng, Y. (2016, January 12\u201317). Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","key":"ref_8","DOI":"10.1609\/aaai.v30i1.10446"},{"doi-asserted-by":"crossref","unstructured":"Park, D., Ramanan, D., and Fowlkes, C. (2010, January 5\u201311). Multiresolution Models for Object Detection. Proceedings of the 11th European Conference on Computer Vision: Part IV (ECCV), Heraklion, Greece.","key":"ref_9","DOI":"10.1007\/978-3-642-15561-1_18"},{"doi-asserted-by":"crossref","unstructured":"Benenson, R., Mathias, M., Timofte, R., and Gool, L.J.V. (2012, January 16\u201321). Pedestrian detection at 100 frames per second. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","key":"ref_10","DOI":"10.1109\/CVPR.2012.6248017"},{"doi-asserted-by":"crossref","unstructured":"Costea, A.D., and Nedevschi, S. (2014, January 23\u201328). Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","key":"ref_11","DOI":"10.1109\/CVPR.2014.307"},{"doi-asserted-by":"crossref","unstructured":"Yan, J., Zhang, X., Lei, Z., Liao, S., and Li, S.Z. (2013, January 23\u201328). Robust Multi-resolution Pedestrian Detection in Traffic Scenes. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","key":"ref_12","DOI":"10.1109\/CVPR.2013.390"},{"unstructured":"Fan, W., Stolfo, S.J., Zhang, J., and Chan, P.K. (1999). AdaCost: Misclassification Cost-Sensitive Boosting. Proceedings of the Seventeenth International Conference on Machine Learning (ICML), Morgan Kaufmann Publishers Inc.","key":"ref_13"},{"unstructured":"Ting, K.M. (2000). A Comparative Study of Cost-Sensitive Boosting Algorithms. Proceedings of the Seventeenth International Conference on Machine Learning (ICML), Morgan Kaufmann Publishers Inc.","key":"ref_14"},{"doi-asserted-by":"crossref","unstructured":"Abe, N., Zadrozny, B., and Langford, J. (2004, January 22\u201325). An iterative method for multi-class cost-sensitive learning. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA.","key":"ref_15","DOI":"10.1145\/1014052.1014056"},{"doi-asserted-by":"crossref","unstructured":"Hwang, S., Park, J., Kim, N., Choi, Y., and Kweon, I.S. (2015, January 7\u201312). Multispectral pedestrian detection: Benchmark dataset and baseline. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","key":"ref_16","DOI":"10.1109\/CVPR.2015.7298706"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s11263-005-6644-8","article-title":"Detecting Pedestrians Using Patterns of Motion and Appearance","volume":"63","author":"Viola","year":"2005","journal-title":"Int. J. Comput. Vis."},{"unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA.","key":"ref_18"},{"doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Tu, Z., Perona, P., and Belongie, S. (2009). Integral Channel Features. Proceedings of the British Machine Vision Conference, BMVC Press.","key":"ref_19","DOI":"10.5244\/C.23.91"},{"doi-asserted-by":"crossref","unstructured":"Sermanet, P., Kavukcuoglu, K., Chintala, S., and LeCun, Y. (2013, January 23\u201328). Pedestrian Detection with Unsupervised Multi-stage Feature Learning. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","key":"ref_20","DOI":"10.1109\/CVPR.2013.465"},{"doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Belongie, S., and Perona, P. (2010). The Fastest Pedestrian Detector in the West, BMVC Press.","key":"ref_21","DOI":"10.5244\/C.24.68"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object Detection with Discriminatively Trained Part-Based Models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai, S., Shen, C., and van den Hengel, A. (2013, January 1\u20138). Efficient Pedestrian Detection by Directly Optimizing the Partial Area under the ROC Curve. Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, Australia.","key":"ref_23","DOI":"10.1109\/ICCV.2013.135"},{"doi-asserted-by":"crossref","unstructured":"Mar\u00edn, J., V\u00e1zquez, D., L\u00f3pez, A.M., Amores, J., and Leibe, B. (2013, January 1\u20138). Random Forests of Local Experts for Pedestrian Detection. Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, Australia.","key":"ref_24","DOI":"10.1109\/ICCV.2013.322"},{"doi-asserted-by":"crossref","unstructured":"Luo, P., Tian, Y., Wang, X., and Tang, X. (2014, January 23\u201328). Switchable Deep Network for Pedestrian Detection. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","key":"ref_25","DOI":"10.1109\/CVPR.2014.120"},{"doi-asserted-by":"crossref","unstructured":"Ouyang, W., Zeng, X., and Wang, X. (2013, January 23\u201328). Modeling Mutual Visibility Relationship in Pedestrian Detection. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","key":"ref_26","DOI":"10.1109\/CVPR.2013.414"},{"doi-asserted-by":"crossref","unstructured":"Mathias, M., Benenson, R., Timofte, R., and Gool, L.J.V. (2013, January 1\u20138). Handling Occlusions with Franken-Classifiers. Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, Australia.","key":"ref_27","DOI":"10.1109\/ICCV.2013.190"},{"doi-asserted-by":"crossref","unstructured":"Ouyang, W., and Wang, X. (2013, January 1\u20138). Joint Deep Learning for Pedestrian Detection. Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, Australia.","key":"ref_28","DOI":"10.1109\/ICCV.2013.257"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Bauckhage, C., and Cremers, A.B. (2014, January 23\u201328). Informed Haar-Like Features Improve Pedestrian Detection. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","key":"ref_29","DOI":"10.1109\/CVPR.2014.126"},{"doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai, S., Shen, C., and van den Hengel, A. (2014, January 6\u201312). Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","key":"ref_30","DOI":"10.1007\/978-3-319-10593-2_36"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/TPAMI.2015.2474388","article-title":"Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning","volume":"38","author":"Paisitkriangkrai","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Benenson, R., Omran, M., Hosang, J.H., and Schiele, B. (2014). Ten Years of Pedestrian Detection, What Have We Learned?. Computer Vision\u2014ECCV 2014 Workshops, Springer.","key":"ref_32","DOI":"10.1007\/978-3-319-16181-5_47"},{"doi-asserted-by":"crossref","unstructured":"Angelova, A., Krizhevsky, A., and Vanhoucke, V. (2015, January 26\u201330). Pedestrian detection with a Large-Field-Of-View deep network. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","key":"ref_33","DOI":"10.1109\/ICRA.2015.7139256"},{"doi-asserted-by":"crossref","unstructured":"Toca, C., Ciuc, M., and Patrascu, C. (2015). Normalized Autobinomial Markov Channels For Pedestrian Detection, BMVC Press.","key":"ref_34","DOI":"10.5244\/C.29.175"},{"doi-asserted-by":"crossref","unstructured":"Angelova, A., Krizhevsky, A., Vanhoucke, V., Ogale, A., and Ferguson, D. (2015). Real-Time Pedestrian Detection with Deep Network Cascades, BMVC Press.","key":"ref_35","DOI":"10.5244\/C.29.32"},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, Z., and Wu, F. (2015). Exploring Prior Knowledge for Pedestrian Detection, BMVC Press.","key":"ref_36","DOI":"10.5244\/C.29.176"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2015, January 7\u201312). Filtered channel features for pedestrian detection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","key":"ref_37","DOI":"10.1109\/CVPR.2015.7298784"},{"doi-asserted-by":"crossref","unstructured":"Tian, Y., Luo, P., Wang, X., and Tang, X. (2015, January 7\u201313). Deep Learning Strong Parts for Pedestrian Detection. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","key":"ref_38","DOI":"10.1109\/ICCV.2015.221"},{"doi-asserted-by":"crossref","unstructured":"Cai, Z., Saberian, M.J., and Vasconcelos, N. (2015, January 7\u201313). Learning Complexity-Aware Cascades for Deep Pedestrian Detection. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","key":"ref_39","DOI":"10.1109\/ICCV.2015.384"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2017, January 21\u201326). CityPersons: A Diverse Dataset for Pedestrian Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_40","DOI":"10.1109\/CVPR.2017.474"},{"key":"ref_41","first-page":"985","article-title":"Scale-aware Fast R-CNN for Pedestrian Detection","volume":"20","author":"Li","year":"2018","journal-title":"IEEE Trans. Multimed."},{"unstructured":"Du, X., El-Khamy, M., Morariu, V.I., Lee, J., and Davis, L.S. (arXiv, 2018). Fused Deep Neural Networks for Efficient Pedestrian Detection, arXiv.","key":"ref_42"},{"doi-asserted-by":"crossref","unstructured":"Song, T., Sun, L., Xie, D., Sun, H., and Pu, S. (arXiv, 2018). Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation, arXiv.","key":"ref_43","DOI":"10.1007\/978-3-030-01234-2_33"},{"doi-asserted-by":"crossref","unstructured":"Brazil, G., Yin, X., and Liu, X. (2017, January 22\u201329). Illuminating Pedestrians via Simultaneous Detection and Segmentation. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","key":"ref_44","DOI":"10.1109\/ICCV.2017.530"},{"doi-asserted-by":"crossref","unstructured":"Park, D., Zitnick, C.L., Ramanan, D., and Doll\u00e1r, P. (2013, January 23\u201328). Exploring Weak Stabilization for Motion Feature Extraction. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","key":"ref_45","DOI":"10.1109\/CVPR.2013.371"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","article-title":"Fast Feature Pyramids for Object Detection","volume":"36","author":"Appel","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Girshick, R.B. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","key":"ref_47","DOI":"10.1109\/ICCV.2015.169"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:32:01Z","timestamp":1760185921000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,14]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19040780"],"URL":"https:\/\/doi.org\/10.3390\/s19040780","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,2,14]]}}}