{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:25:57Z","timestamp":1760243157337,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2015,12,21]],"date-time":"2015-12-21T00:00:00Z","timestamp":1450656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61302121"],"award-info":[{"award-number":["61302121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians\u2019 head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians\u2019 size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only.<\/jats:p>","DOI":"10.3390\/s151229874","type":"journal-article","created":{"date-parts":[[2015,12,21]],"date-time":"2015-12-21T10:43:59Z","timestamp":1450694639000},"page":"32188-32212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4509-8991","authenticated-orcid":false,"given":"Guohua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software Engineering, South China University of Technology, No. 382 Waihuan East Rd., Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, South China University of Technology, No. 382 Waihuan East Rd., Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ger\u00f3nimo, D., and L\u00f3pez, A.M. (2013). Vision-Based Pedestrian Protection Systems for Intelligent Vehicles, Springer.","DOI":"10.1007\/978-1-4614-7987-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.neucom.2010.10.009","article-title":"Pyramid binary pattern features for real-time pedestrian detection from infrared videos","volume":"74","author":"Sun","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.infrared.2013.06.003","article-title":"Robust and fast pedestrian detection method for far-infrared automotive driving assistance systems","volume":"60","author":"Liu","year":"2013","journal-title":"Infrared Phys. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"347","DOI":"10.3233\/ICA-130441","article-title":"Pedestrian detection in far infrared images","volume":"20","author":"Olmeda","year":"2013","journal-title":"Integrated Comput.-Aided Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.infrared.2014.07.022","article-title":"Hybrid multi-resolution detection of moving targets in infrared imagery","volume":"67","author":"Tewary","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wu, Z., Fuller, N., Theriault, D., and Betke, M. (2014, January 23\u201328). A thermal infrared video benchmark for visual analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.39"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11263-006-4121-7","article-title":"Background-subtraction in thermal imagery using contour saliency","volume":"71","author":"Davis","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00138-013-0570-5","article-title":"Thermal cameras and applications: A survey","volume":"25","author":"Gade","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_9","unstructured":"Portmann, J., Lynen, S., Chli, M., and Siegwart, R. (June, January 31). People detection and tracking from aerial thermal views. Proceedings of the IEEE Conference on Robotics and Automation, Hong Kong, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/TITS.2009.2018961","article-title":"Real-time pedestrian detection and tracking at nighttime for driver-assistance systems","volume":"10","author":"Ge","year":"2009","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113105","DOI":"10.1117\/1.OE.52.11.113105","article-title":"Human tracking in thermal images using adaptive particle filters with online random forest learning","volume":"52","author":"Ko","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Miron, A., Besbes, B., Rogozan, A., Ainouz, S., and Bensrhair, A. (2012, January 3\u20137). Intensity self similarity features for pedestrian detection in far-infrared images. Proceedings of the IEEE Conference on Intelligent Vehicles Symposium, Alcala de Henares, Spain.","DOI":"10.1109\/IVS.2012.6232227"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Olmeda, D., de la Escalera, A., and Armingol, J.M. (2012, January 3\u20137). Contrast invariant features for human detection in far infrared images. Proceedings of the IEEE Conference on Intelligent Vehicles Symposium, Alcala de Henares, Spain.","DOI":"10.1109\/IVS.2012.6232242"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1109\/TPAMI.2009.122","article-title":"Survey of pedestrian detection for advanced driver assistance systems","volume":"32","author":"Geronimo","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","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":"Dollar","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.infrared.2013.08.001","article-title":"Segment-based region of interest generation for pedestrian detection in far-infrared images","volume":"61","author":"Kim","year":"2013","journal-title":"Infrared Phys. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kancharla, T., Kharade, P., Gindi, S., Kutty, K., and Vaidya, V.G. (2011, January 3\u20135). Edge based segmentation for pedestrian detection using nir camera. Proceedings of the IEEE Conference on Image Information Processing, Himachal Pradesh, India.","DOI":"10.1109\/ICIIP.2011.6108965"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1016\/j.patcog.2015.04.024","article-title":"Novel outline features for pedestrian detection system with thermal images","volume":"48","author":"Lin","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bertozzi, M., Broggi, A., Gornez, C.H., Fedriga, R.I., Vezzoni, G., and del Rose, M. (2007, January 13\u201315). Pedestrian detection in far infrared images based on the use of probabilistic templates. Proceedings of the IEEE Conference on Intelligent Vehicles Symposium, Istanbul, Turkey.","DOI":"10.1109\/IVS.2007.4290135"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.infrared.2010.03.005","article-title":"Robust pedestrian detection in thermal infrared imagery using the wavelet transform","volume":"53","author":"Li","year":"2010","journal-title":"Infrared Phys. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Qi, B., John, V., Liu, Z., and Mita, S. (2014, January 8\u201311). Pedestrian detection from thermal images with a scattered difference of directional gradients feature descriptor. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Qingdao, China.","DOI":"10.1109\/ITSC.2014.6958024"},{"key":"ref_22","unstructured":"Meis, U., Oberlander, M., and Ritter, W. (2004, January 14\u201317). Reinforcing the reliability of pedestrian detection in far-infrared sensing. Proceedings of the IEEE Conference on Intelligent Vehicles Symposium, Parma, Italy."},{"key":"ref_23","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1049\/el.2012.4261","article-title":"Histograms of local intensity differences for pedestrian classification in far-infrared images","volume":"49","author":"Kim","year":"2013","journal-title":"Electron. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","first-page":"347","article-title":"Multispectral pedestrian detection: Benchmark dataset and baseline","volume":"20","author":"Hwang","year":"2013","journal-title":"Integrated Comput.-Aided Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","article-title":"Fast feature pyramids for object detection","volume":"36","author":"Dollar","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11263-006-9038-7","article-title":"Multi-cue pedestrian detection and tracking from a moving vehicle","volume":"73","author":"Gavrila","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1016\/j.eswa.2011.09.106","article-title":"Pedestrian detection for intelligent transportation systems combining adaboost algorithm and support vector machine","volume":"39","author":"Guo","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"John, V., Mita, S., Liu, Z., and Qi, B. (2015, January 18\u201322). Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neuralnetworks. Proceedings of the IEEE Conference on Machine Vision Applications Proceedings, Tokyo, Japan.","DOI":"10.1109\/MVA.2015.7153177"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dollar, P., Appel, R., and Kienzle, W. (2012, January 7\u201313). Crosstalk cascades for frame-rate pedestrian detection. Proceedings of the IEEE Conference on European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33709-3_46"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cheng, M.-M., Zhang, Z., Lin, W.-Y., and Torr, P. (2014, January 23\u201328). Bing: Binarized normed gradients for objectness estimation at 300 fps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.414"},{"key":"ref_33","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."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhuang, J., and Kong, S. (2013). Detection of pedestrians for far-infrared automotive night vision systems using learning-based method and head validation. Meas. Sci. Technol., 24.","DOI":"10.1088\/0957-0233\/24\/7\/074022"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Vazquez, D., Xu, J.L., Ramos, S., Lopez, A.M., and Ponsa, D. (2013, January 23\u201328). Weakly supervised automatic annotation of pedestrian bounding boxes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA.","DOI":"10.1109\/CVPRW.2013.107"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chang, C.C., and Lin, C.J. (2011). Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2.","DOI":"10.1145\/1961189.1961199"},{"key":"ref_37","unstructured":"LSI Far Infrared Pedestrian Dataset. Available online: http:\/\/www.uc3m.es\/islab\/repository."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1177\/0278364912470012","article-title":"Fusing lidar, camera and semantic information: A context-based approach for pedestrian detection","volume":"32","author":"Premebida","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_40","unstructured":"Open Source Computer Vision Library. Available online: http:\/\/wiki.opencv.org.cn\/index.php\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/12\/29874\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:54:28Z","timestamp":1760216068000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/12\/29874"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,12,21]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2015,12]]}},"alternative-id":["s151229874"],"URL":"https:\/\/doi.org\/10.3390\/s151229874","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2015,12,21]]}}}