{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:37:17Z","timestamp":1776681437094,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T00:00:00Z","timestamp":1471564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61371076"],"award-info":[{"award-number":["61371076"]}],"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","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles\u2019 in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.<\/jats:p>","DOI":"10.3390\/s16081325","type":"journal-article","created":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T09:58:27Z","timestamp":1471600707000},"page":"1325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":110,"title":["A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Yongzheng","family":"Xu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China"}]},{"given":"Guizhen","family":"Yu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China"}]},{"given":"Yunpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China"}]},{"given":"Xinkai","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China"}]},{"given":"Yalong","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16865","DOI":"10.3390\/rs71215858","article-title":"Autonomous Chemical Vapour Detection by Micro UAV","volume":"7","author":"Rosser","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3390\/s16010097","article-title":"Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation","volume":"16","author":"Gonzalez","year":"2016","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15717","DOI":"10.3390\/s150715717","article-title":"UAV Deployment Exercise for Mapping Purposes: Evaluation of Emergency Response Applications","volume":"15","author":"Boccardo","year":"2015","journal-title":"Sensors"},{"key":"ref_4","unstructured":"Agrawal, A., and Hickman, M. (2004, January 3\u20136). Automated extraction of queue lengths from airborne imagery. Proceedings of the International IEEE Conference on Intelligent Transportation Systems, Washington, DC, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1049\/ip-its:20055014","article-title":"Roadway traffic monitoring from an unmanned aerial vehicle","volume":"153","author":"Coifman","year":"2006","journal-title":"IEE Proc. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1540038","DOI":"10.1142\/S0218127415400386","article-title":"Measuring algorithm for the distance to a preceding vehicle on curve road using on-board monocular camera","volume":"25","author":"Yu","year":"2015","journal-title":"Int. J. Bifurc. Chaos"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/TITS.2003.821208","article-title":"Methods of analyzing traffic imagery collected from aerial platforms","volume":"4","author":"Angel","year":"2003","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.sbspro.2014.01.119","article-title":"Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing","volume":"111","author":"Azevedo","year":"2014","journal-title":"Procedia Soc. Behav. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TITS.2005.858621","article-title":"Airborne video registration and traffic-flow parameter estimation","volume":"6","author":"Shastry","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","unstructured":"Yalcin, H., Hebert, M., Collins, R., and Black, M.J. (2005, January 20\u201325). A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_11","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA."},{"key":"ref_12","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, San Diego, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cao, X., Wu, C., Yan, P., and Li, X. (2011, January 11\u201314). Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos. Proceedings of the IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116132"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/TCSVT.2011.2162274","article-title":"Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment","volume":"21","author":"Cao","year":"2011","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11315","DOI":"10.3390\/rs61111315","article-title":"An Operational System for Estimating Road Traffic Information from Aerial Images","volume":"6","author":"Leitloff","year":"2014","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1109\/JSTARS.2013.2242846","article-title":"Airborne Vehicle Detection in Dense Urban Areas Using HoG Features and Disparity Maps","volume":"6","author":"Tuermer","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, Y., Yu, G., Wang, Y., and Wu, X. (2015, January 24\u201327). Vehicle Detection and Tracking from Airborne Images. Proceedings of the 15th COTA International Conference of Transportation Professionals, Beijing, China.","DOI":"10.1061\/9780784479292.059"},{"key":"ref_18","unstructured":"Jones, M., and Viola, P. (2003, January 16\u201322). Fast Multi-view Face Detection. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6356","DOI":"10.1109\/TGRS.2013.2296351","article-title":"Detecting cars in UAV images with a catalog-based approach","volume":"52","author":"Moranduzzo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TGRS.2013.2253108","article-title":"Automatic car counting method for unmanned aerial vehicle images","volume":"52","author":"Moranduzzo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/LGRS.2015.2439517","article-title":"Fast Multiclass Vehicle Detection on Aerial Images","volume":"12","author":"Liu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1214\/aos\/1016218223","article-title":"Additive Logistic Regression: A Statistical View of Boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/6979.880969","article-title":"Image analysis and rule-based reasoning for a traffic monitoring system","volume":"1","author":"Cucchiara","year":"2000","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/34.982885","article-title":"Object tracking with Bayesian estimation of dynamic layer representations","volume":"24","author":"Tao","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"446","DOI":"10.3390\/s16040446","article-title":"Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery","volume":"16","author":"Ma","year":"2016","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TPAMI.2008.300","article-title":"LSD: A Fast Line Segment Detector with a False Detection Control","volume":"32","author":"Grompone","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/0031-3203(81)90009-1","article-title":"Generalizing the Hough Transform to Detect Arbitrary Shapes","volume":"13","author":"Ballard","year":"1981","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5201\/ipol.2012.gjmr-lsd","article-title":"LSD: A Line Segment Detector","volume":"2","author":"Gioi","year":"2012","journal-title":"Image Process. Line"},{"key":"ref_30","first-page":"5","article-title":"Traffic Flow Theory","volume":"1","author":"Maerivoet","year":"2005","journal-title":"Physics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TIP.2010.2101613","article-title":"ViBe: A universal background subtraction algorithm for video sequences","volume":"20","author":"Olivier","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1142\/S0218001492000229","article-title":"The Depth and Motion Analysis Machine","volume":"6","author":"Faugeras","year":"1992","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TPAMI.1986.4767808","article-title":"Extracting Straight Lines","volume":"8","author":"Burns","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","first-page":"679","article-title":"A computational approach to edge detection","volume":"8","author":"Canny","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","unstructured":"Uyttendaele, M., Eden, A., and Skeliski, R. (2001, January 8\u201314). Eliminating Ghosting and Exposure Artifacts in Image Mosaics. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Tu, Z., Perona, P., and Belongie, S. (2009, January 7\u201310). Integral Channel Features. Proceedings of the British Machine Vision Conference, London, UK.","DOI":"10.5244\/C.23.91"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","article-title":"Vehicle detection in satellite images by hybrid deep convolutional neural networks","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Neural Information Processing Systems Conference, Montreal, QC, Canada."},{"key":"ref_39","unstructured":"Faster R-CNN. Available online: https:\/\/github.com\/rbgirshick\/py-faster-rcnn."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/8\/1325\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:28:45Z","timestamp":1760210925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/8\/1325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,19]]},"references-count":39,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2016,8]]}},"alternative-id":["s16081325"],"URL":"https:\/\/doi.org\/10.3390\/s16081325","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,8,19]]}}}