{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T03:17:36Z","timestamp":1761621456651,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we describe a robust method for compensating the panning and tilting motion of a camera, applied to foreground\u2013background segmentation. First, the necessary internal camera parameters are determined through feature-point extraction and tracking. From these parameters, two motion models for points in the image plane are established. The first model assumes a fixed tilt angle, whereas the second model allows simultaneous pan and tilt. At runtime, these models are used to compensate for the motion of the camera in the background model. We will show that these methods provide a robust compensation mechanism and improve the foreground masks of an otherwise state-of-the-art unsupervised foreground\u2013background segmentation method. The resulting algorithm is always able to obtain F 1 scores above 80 % on every daytime video in our test set when a minimal number of only eight feature matches are used to determine the background compensation, whereas the standard approaches need significantly more feature matches to produce similar results.<\/jats:p>","DOI":"10.3390\/s19122668","type":"journal-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T11:15:58Z","timestamp":1560424558000},"page":"2668","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Robust Pan\/Tilt Compensation for Foreground\u2013Background Segmentation"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2502-3746","authenticated-orcid":false,"given":"Gianni","family":"Allebosch","sequence":"first","affiliation":[{"name":"TELIN-IPI, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium"},{"name":"imec, Kapeldreef 75, B-3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Van Hamme","sequence":"additional","affiliation":[{"name":"TELIN-IPI, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium"},{"name":"imec, Kapeldreef 75, B-3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4746-9087","authenticated-orcid":false,"given":"Peter","family":"Veelaert","sequence":"additional","affiliation":[{"name":"TELIN-IPI, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium"},{"name":"imec, Kapeldreef 75, B-3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wilfried","family":"Philips","sequence":"additional","affiliation":[{"name":"TELIN-IPI, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium"},{"name":"imec, Kapeldreef 75, B-3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yi, K.M., Yun, K., Kim, S.W., Chang, H.J., and Choi, J.Y. (2013, January 23\u201328). Detection of moving objects with non-stationary cameras in 5.8ms: Bringing motion detection to your mobile device. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA.","DOI":"10.1109\/CVPRW.2013.9"},{"key":"ref_2","unstructured":"Kim, D.S., and Kwon, J. (2016). Moving object detection on a vehicle mounted back-up camera. Sensors, 16."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/978-3-319-29971-6_23","article-title":"C-EFIC: Color and edge based foreground background segmentation with interior classification","volume":"Volume 598","author":"Allebosch","year":"2016","journal-title":"Computer Vision, Imaging and Computer Graphics Theory and Applications"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_5","unstructured":"Tsai, Y.H., Yang, M.H., and Black, M.J. (July, January 26). Video segmentation via object flow. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taix\u00e9, L., Cremers, D., and Van Gool, L. (2017, January 21\u201326). One-Shot Video Object Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hi, USA.","DOI":"10.1109\/CVPR.2017.565"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., and Sorkine-Hornung, A. (2017, January 21\u201326). Learning Video Object Segmentation from Static Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hi, USA.","DOI":"10.1109\/CVPR.2017.372"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wehrwein, S., and Szeliski, R. (2017, January 4\u20137). Video Segmentation with Background Motion Models. Proceedings of the BMVC, London, UK.","DOI":"10.5244\/C.31.96"},{"key":"ref_9","unstructured":"Stauffer, C., and Grimson, W.E.L. (1999, January 23\u201325). Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"219","DOI":"10.2174\/2213275910801030219","article-title":"Background Modeling Using Mixture of Gaussians for Foreground Detection\u2014A Survey","volume":"1","author":"Bouwmans","year":"2008","journal-title":"Recent Pat. Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"343057","DOI":"10.1155\/2010\/343057","article-title":"Background subtraction for automated multisensor surveillance: A comprehensive review","volume":"2010","author":"Cristani","year":"2010","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_12","unstructured":"Kim, K., Chalidabhongse, T.H., Harwood, D., and Davis, L. (2004, January 24\u201327). Background Modeling and Subtraction by Codebook Construction. Proceedings of the International Conference on Image Processing (ICIP), Singapore."},{"key":"ref_13","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":"Barnich","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Van Droogenbroeck, M., and Paquot, O. (2012, January 16\u201321). Background subtraction: Experiments and improvements for ViBe. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238924"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1049\/el.2013.1944","article-title":"Efficient foreground detection for real-time surveillance applications","volume":"49","author":"Petrovic","year":"2013","journal-title":"Electron. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TPAMI.2006.68","article-title":"A texture-based method for modeling the background and detecting moving objects","volume":"28","author":"Heikkila","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TIP.2014.2378053","article-title":"SuBSENSE: A universal change detection method with local adaptive sensitivity","volume":"24","author":"Bilodeau","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"St-Charles, P.L., Bilodeau, G.A., and Bergevin, R. (2015, January 5\u20139). A self-adjusting approach to change detection based on background word consensus. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.137"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Allebosch, G., Van Hamme, D., Deboeverie, F., Veelaert, P., and Philips, W. (2015, January 11\u201314). Edge based foreground background estimation with interior\/exterior Classification. Proceedings of the 10th International Conference on Computer Vision Theory and Applications, Berlin, Germany.","DOI":"10.5220\/0005358003690376"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hofmann, M., Tiefenbacher, P., and Rigoll, G. (2012, January 13\u201321). Background segmentation with Feedback: The Pixel-Based Adaptive Segmenter. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238925"},{"key":"ref_21","unstructured":"Mittal, A., and Huttenlocher, D. (2000, January 13\u201315). Scene modeling for wide area surveillance and image synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., and Ishwar, P. (2014, January 23\u201328). CDnet 2014: An Expanded Change Detection Benchmark Dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.126"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22489","DOI":"10.1007\/s11042-018-6104-4","article-title":"Foreground segmentation with PTZ camera: A survey","volume":"77","author":"Komagal","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","unstructured":"Hartley, R.I., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press. [2nd ed.]."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1109\/TPAMI.2003.1217605","article-title":"The Effects of Translational Misalignment when Self-Calibrating Rotating and Zooming Cameras","volume":"25","author":"Hayman","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, Y.C., and Chiu, C.T. (2017, January 5\u20139). Motion clustering with hybrid-sample-based foreground segmentation for moving cameras. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952346"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1364\/JOSAA.32.000156","article-title":"Method for pan-tilt camera calibration using single control point","volume":"32","author":"Li","year":"2015","journal-title":"J. Opt. Soc. Am. A Opt. Image Sci. Vis."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhu, F., and Little, J.J. (2018, January 12\u201315). A Two-Point Method for PTZ Camera Calibration in Sports. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00038"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s00138-011-0326-z","article-title":"Optimizing PTZ Camera Calibration from Two Images","volume":"23","author":"Junejo","year":"2012","journal-title":"Mach. Vis. Appl."},{"key":"ref_30","unstructured":"de Agapito, L., Hartley, R.I., and Hayman, E. (1999, January 23\u201325). Linear self-calibration of a rotating and zooming camera. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_32","unstructured":"Muja, M., and Lowe, D.G. (2009, January 5\u20138). Fast approximate nearest neighbors with automatic algorithm configuration. Proceedings of the VISAPP International Conference on Computer Vision Theory and Applications, Lisboa, Portugal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0146-664X(82)90101-0","article-title":"Fitting conic sections to \u201cvery scattered\u201d data: An iterative refinement of the bookstein algorithm","volume":"18","author":"Sampson","year":"1982","journal-title":"Comput. Graph. Image Process."},{"key":"ref_34","first-page":"5885","article-title":"Improvements to the Levenberg-Marquardt Algorithm for nonlinear least-squares minimization","volume":"1201","author":"Transtrum","year":"2012","journal-title":"arXiv"},{"key":"ref_35","unstructured":"Bouguet, J.Y. (2019, June 03). Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm. Available online: https:\/\/pdfs.semanticscholar.org\/aa97\/2b40c0f8e20b07e02d1fd320bc7ebadfdfc7.pdf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TIP.2010.2042645","article-title":"Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions","volume":"19","author":"Tan","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","unstructured":"Turkowski, K. (1990). Graphics Gems, Academic Press Professional, Inc."},{"key":"ref_38","unstructured":"Shi, J., and Tomasi, C. (1994, January 21\u201323). Good features to track. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A Flexible New Technique for Camera Calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.patrec.2018.08.002","article-title":"Foreground segmentation using convolutional neural networks for multiscale feature encoding","volume":"112","author":"Lim","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Braham, M., Pi\u00e9rard, S., and Van Droogenbroeck, M. (2017, January 17\u201320). Semantic background subtraction. Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297144"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2668\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:58:04Z","timestamp":1760187484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2668"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,13]]},"references-count":41,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19122668"],"URL":"https:\/\/doi.org\/10.3390\/s19122668","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,6,13]]}}}