An operational system for estimating road traffic information from aerial images

Remote Sensing

Volumn 6, Issue 11, 2014, Pages 11315-11341

  Leitloff, Jens ^a   Rosenbaum, Dominik ^a   Kurz, Franz ^a   Meynberg, Oliver ^a   Reinartz, Peter ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Georeferencing; Monitoring; Orthorectification; Pattern recognition; Sequences; Tracking; Vehicle detection

Indexed keywords

ADAPTIVE BOOSTING; AIRCRAFT ACCIDENTS; CHAINS; COMPUTER VISION; DATA MINING; DISASTERS; GEOMETRICAL OPTICS; MONITORING; PATTERN RECOGNITION; ROADS AND STREETS; SENSOR NETWORKS; SUPPORT VECTOR MACHINES; SURFACE DISCHARGES; TRAFFIC CONTROL; VEHICLES;

AUTOMATIC EXTRACTION; GEOREFERENCING; OPERATIONAL SYSTEMS; ORTHO-RECTIFICATION; ROAD TRAFFIC INFORMATIONS; SEQUENCES; SUPPORT VECTOR MACHINE CLASSIFIERS; VEHICLE DETECTION;

TRACKING (POSITION);

EID: 84912110068     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs61111315     Document Type: Article

References (88)

1. Schäfer, R.; Thiessenhusen, K.; Wagner, P. A traffic information system by means of real-time floating-car data. In Proceedings of the ITS World Congress, Chicago, IL, USA, 11-14 October 2002; Volume 2.
2. Lorkowski, S.; Brockfeld, E.; Mieth, P.; Passfeld, B.; Thiessenhusen, K.; Schäfer, R.; Für Luft, D. Erste Mobilitätsdienste auf Basis von "Floating Car Data". Available online: http://elib.dlr.de/6613/1/Vortrag.pdf (accessed on 7 November 2014).
3. Busch, F.; Glas, F.; Bermann, E. Dispositionssysteme als FCD-Quellen für eine verbesserte Verkehrslagerekonstruktion in Städten. Straßenverkehrstechnik 2004, 9, 437-444.
4. Geisler, S.; Chen, Y.; Quix, C.; Gehlen, G. Accuracy assessment for traffic information derived from floating phone data. In Proceedings of the Intelligent Transportation Systems and Services World Congress, Busan, Korea, 25-29 October 2010.
5. Brockfeld, E.; Lorkowski, S.; Mieth, P.; Wagner, P. Benefits and limits of recent floating car data technology-An evaluation study. In Proceedings of the 11th WCTR Conference, Berkeley, CA, USA, 24-28 June 2007; pp. 24-28.
6. Ernst, I.; Sujew, S.; Thiessenhusen, K.; Hetscher, M.; Rassmann, S.; Ruhé, M. LUMOS-airborne traffic monitoring system. In Proceedings of the 2003 Intelligent Transportation Systems, Shanghai, China, 12-15 October 2003; Volume 1, pp. 753-759.
7. Börner, A.; Ernst, I.; Ruhé, M.; Sujew, S.; Hetscher, M. Airborne camera experiments for traffic monitoring. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2004, 35, 243-248.
8. Rosenbaum, D.; Charmette, B.; Kurz, F.; Suri, S.; Thomas, U.; Reinartz, P. Automatic traffic monitoring from an airborne wide angle camera system. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 37, 557-562.
9. Baumgartner, S.; Krieger, G. A priori knowledge based GMTI algorithm for traffic monitoring applications. In Proceedings of the 2010 8th European Conference on Synthetic Aperture Radar (EUSAR 2010), Aachen, Germany, 7-10 June 2010; pp. 1-4.
10. Kurz, F.; Türmer, S.; Meynberg, O.; Rosenbaum, D.; Leitloff, J.; Runge, H.; Reinartz, P. Low-cost optical camera systems for real time mapping applications. Photogramm. Fernerkund. Geoinf. 2012, 2012, 159-176.
11. Horwath, J.; Fuchs, C. Aircraft to ground unidirectional laser-communications terminal for high-resolution sensors. Proc. SPIE 2009, doi:10.1117/12.808869.
12. Römer, H.; Kersten, J.; Kiefl, R.; Plattner, S.; Mager, A.; Voigt, S. Airborne near-real-time monitoring of assembly and parking areas in case of large-scale public events and natural disasters. Int. J. Geogr. Inf. Sci. 2014, 28, 682-699.
13. Frassl, M.; Lichtenstern, M.; Khider, M.; Angermann, M. Developing a system for information management in disaster relief-Methodology and requirements. In Proceedings of the 7th International ISCRAM Conference, Seattle, WA, USA, 2-5 May 2010.
14. Behrisch, M.; Bonert, M.; Brockfeld, E.; Krajzewicz, D.; Wagner, P. Event traffic forecast for metropolitan areas based on microscopic simulation. In Proceedings of the Third International Symposium of Transport Simulation 2008 (ISTS08), Surfers Paradise, QLD, Australia, 6-8 August 2008.
15. Thomas, U.; Rosenbaum, D.; Kurz, F.; Suri, S.; Reinartz, P. A new software/hardware architecture for real time image processing of wide area airborne camera images. J. Real-Time Image Process. 2009, 4, 229-244.
16. Stilla, U.; Michaelsen, E.; Sörgel, U.; Hinz, S.; Ender, J. Airborne monitoring of vehicle activity in urban areas. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2004, 35, 973-979.
17. Hinz, S.; Bamler, R.; Stilla, U. Theme issue: "Airborne and spaceborne traffic monitoring". ISPRS J. Photogramm. Remote Sens. 2006, 61, 135-136.
18. Ramakrishnan, V.; Kethsy Prabhavathy, A.; Devishree, J. A survey on vehicle detection techniques in aerial surveillance. Int. J. Comput. Appl. 2012, 55, 43-47.
19. Burlina, P.; Parameswaran, V.; Chellappa, R. Sensitivity analysis and learning strategies for context-based vehicle detection algorithms. In Proceedings of the DARPA Image Understanding Workshop, New Orleans, LA, USA, 11-14 May 1997; pp. 577-584.
20. Moon, H.; Chellappa, R.; Rosenfeld, A. Performance analysis of a simple vehicle detection algorithm. Image Vis. Comput. 2002, 20, 1-13.
21. Zhao, T.; Nevatia, R. Car detection in low resolution aerial images. Image Vis. Comput. 2003, 21, 693-703.
22. Hinz, S. Detection of vehicles and vehicle queues in high resolution aerial images. Photogramm. Fernerkund. Geoinf. 2004, 3, 201-213.
23. Ruskoné, R.; Guiges, L.; Airault, S.; Jamet, O. Vehicle detection on aerial images: A structural approach. In Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria, 25-29 August 1996; Kropatsch, G., Ed.; IEEE Computer Society Press: Vienna, Austria, 1996; Volume 3, pp. 900-903.
24. Spiegel, M. Automatische Erkennung von Fahrzeugen aus digitalen Luftbildern mittels Neuronaler Netze. Master's Thesis, Technische Universität München, Munich, Germany, 2000.
25. Grabner, H.; Nguyen, T.T.; Gruber, B.; Bischof, H. On-line boosting-based car detection from aerial images. ISPRS J. Photogramm. Remote Sens. 2008, 63, 382-396.
26. Kembhavi, A.; Harwood, D.; Davis, L.S. Vehicle detection using partial least squares. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33, 1250-1265.
27. Türmer, S.; Kurz, F.; Reinartz, P.; Stilla, U. Airborne vehicle detection in dense urban areas using HoG features and disparity maps. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2013, 6, 2327-2337.
28. Dalal, N.; Triggs, B. Histograms of oriented gradients for human detection. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA, 20-26 June 2005; Volume 1, pp. 886-893.
29. Gaszczak, A.; Breckon, T.P.; Han, J. Real-time people and vehicle detection from UAV imagery. In Proceedings of the IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, San Fransico, CA, USA, 23 January 2011.
30. Gleason, J.; Nefian, A.; Bouyssounousse, X.; Fong, T.; Bebis, G. Vehicle detection from aerial imagery. In Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, China, 9-13 May 2011; pp. 2065-2070.
31. Reilly, V.; Solmaz, B.; Shah, M. Shadow Casting Out Of Plane (SCOOP) candidates for human and vehicle detection in aerial imagery. Int. J. Comput. Vis. 2013, 101, 350-366.
32. Moranduzzo, T.; Melgani, F. Detecting cars in UAV images with a catalog-based approach. IEEE Trans. Geosci. Remote Sens. 2014, 52, 6356-6367.
33. Kurz, F.; Meynberg, O.; Rosenbaum, D.; Türmer, S.; Reinartz, P.; Schroeder, M. Low-cost optical camera system for disaster monitoring. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, 39, 33-37.
34. Dreschler, L.; Nagel, H.H. Volumetric model and 3-D trajectory of a moving car derived from monocular TV frame sequences of a street scene. Comput. Graph. Image Process. 1982, 20, 199-228.
35. Haag, M.; Nagel, H.H. Combination of edge element and optical flow estimates for 3D-model-based vehicle tracking in traffic image sequences. Int. J. Comput. Vis. 1999, 35, 295-319.
36. Jolly, M.P.D.; Lakshmanan, S.; Jain, A.K. Vehicle segmentation and classification using deformable templates. IEEE Trans. Pattern Anal. Mach. Intell. 1996, 18, 293-308.
37. Tan, T.N.; Sullivan, G.D.; Baker, K.D. Model-based localisation and recognition of road vehicles. Int. J. Comput. Vis. 1998, 27, 5-25.
38. Lachaise, M. Automatic Detection of Vehicles and Velocities in Aerial Digital Image Series. Master's Thesis, Ecole Superieure de Chimie Physique Electronique de Lyon, Lyon, France, 2005.
39. Reinartz, P.; Lachaise, M.; Schmeer, E.; Krauss, T.; Runge, H. Traffic monitoring with serial images from airborne cameras. ISPRS J. Photogramm. Remote Sens. 2006, 61, 149-158.
40. Kozempel, K.; Reulke, R. Fast vehicle detection and tracking in aerial image bursts. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2009, 38, 175-180.
41. Lenhart, D.; Hinz, S.; Leitloff, J.; Stilla, U. Automatic traffic monitoring based on aerial image sequences. Pattern Recogn. Image Anal. 2008, 18, 400-405.
42. Butenuth, M.; Reinartz, P.; Lenhart, D.; Rosenbaum, D.; Hinz, S. Analysis of image sequences for the detection and monitoring of moving traffic. Photogramm. Fernerkund. Geoinf. 2009, 2009, 421-430.
43. Cao, X.; Shi, Z.; Yan, P.; Li, X. Collaborative Kalman filters for vehicle tracking. In Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011), Beijing, China, 18-21 September 2011; pp. 1-6.
44. Szottka, I.; Butenuth, M. Advanced particle filtering for airborne vehicle tracking in urban areas. IEEE Geosci. Remote Sens. Lett. 2014, 11, 686-690.
45. Zheng, H.; Pan, L.; Li, L. A morphological neural network approach for vehicle detection from high resolution satellite imagery. In Neural Information Processing; Springer: Berlin/Heidelberg, Germany, 2006; pp. 99-106.
46. Jin, X.; Davis, C.H. Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks. Image Vis. Comput. 2007, 25, 1422-1431.
47. Eikvil, L.; Aurdal, L.; Koren, H. Classification-based vehicle detection in high-resolution satellite images. ISPRS J. Photogramm. Remote Sens. 2009, 64, 65-72.
48. Leitloff, J.; Hinz, S.; Stilla, U. Vehicle detection in very high resolution satellite images of city areas. IEEE Trans. Geosci. Remote Sens. 2010, 48, 2795-2806.
49. Larsen, S.Ø.; Salberg, A.B.; Eikvil, L. Automatic system for operational traffic monitoring using very-high-resolution satellite imagery. Int. J. Remote Sens. 2013, 34, 4850-4870.
50. Leitloff, J.; Hinz, S.; Stilla, U. Inferring traffic activity from optical satellite images. In Proceedings of the Photogrammetric Image Analysis (PIA07), Munich, Germany, 19-21 September 2007; pp. 89-93.
51. Salehi, B.; Zhang, Y.; Zhong, M. Automatic moving vehicles information extraction from single-pass WorldView-2 imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2012, 5, 135-145.
52. Gao, F.; Li, B.; Xu, Q.; Zhong, C. Moving vehicle information extraction from single-pass WorldView-2 imagery based on ERGAS-SNS analysis. Remote Sens. 2014, 6, 6500-6523.
53. Yao, W.; Hinz, S.; Stilla, U. Extraction and motion estimation of vehicles in single-pass airborne LiDAR data towards urban traffic analysis. ISPRS J. Photogramm. Remote Sens. 2011, 66, 260-271.
54. Yao, W.; Zhang, M.; Hinz, S.; Stilla, U. Airborne traffic monitoring in large areas using LiDAR data-Theory and experiments. Int. J. Remote Sens. 2012, 33, 3930-3945.
55. Borcs, A.; Benedek, C. Extraction of vehicle groups in airborne Lidar point clouds with two-level point processes. IEEE Trans. Geosci. Remote Sens. 2015, 53, 1475-1489.
56. Meyer, F.; Hinz, S.; Laika, A.; Weihing, D.; Bamler, R. Performance analysis of the TerraSAR-X traffic monitoring concept. ISPRS J. Photogramm. Remote Sens. 2006, 61, 225-242.
57. Budillon, A.; Pascazio, V.; Schirinzi, G. Estimation of radial velocity of moving targets by along-track interferometric SAR systems. IEEE Geosci. Remote Sens. Lett. 2008, 5, 349-353.
58. Guo, B.; Vu, D.; Xu, L.; Xue, M.; Li, J. Ground moving target indication via multichannel airborne SAR. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3753-3764.
59. Yao, W.; Hinz, S.; Stilla, U. Automatic analysis of traffic scenario from airborne thermal infrared video. Int. Arch. Photogramm. Remote Sens. Spat. Geoinf. Sci. 2008, 37, 223-228.
60. Baumgartner, S.V.; Krieger, G. Fast GMTI algorithm for traffic monitoring based on a priori knowledge. IEEE Trans. Geosci. Remote Sens. 2012, 50, 4626-4641.
61. Horn, R. The DLR airborne SAR project E-SAR. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS'96), Lincoln, NE, USA, 27-31 May 1996; Volume 3, pp. 1624-1628.
62. Müller, R.; Lehner, M.; Muller, R.; Reinartz, P.; Schroeder, M.; Vollmer, B. A program for direct georeferencing of airborne and spaceborne line scanner images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2002, 34, 148-153.
63. Lienhart, R.; Kuranov, A.; Pisarevsky, V. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Pattern Recogn. 2003, 2781, 297-304.
64. Papageorgiou, C.; Poggio, T. A trainable system for object detection. Int. J. Comput. Vis. 2000, 38, 15-33.
65. Viola, P.; Jones, M.J. Robust real-time face detection. Int. J. Comput. Vis. 2004, 57, 137-154.
66. Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 2000, 28, 337-407.
67. Ferreira, A.; Figueiredo, M. Boosting algorithms: A review of methods, theory and applications. In Ensemble Machine Learning; Springer: New York, NY, USA, 2012; pp. 35-85.
68. Freund, Y.; Schapire, R. A desicion-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory; Springer: Berlin Heidelberg, Germany, 1995; pp. 23-37.
69. Schapire, R.E.; Singer, Y. Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 1999, 37, 297-336.
70. Lepetit, V.; Fua, P. Keypoint recognition using randomized trees. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28, 1465-1479.
71. Vapnik, V. Estimation of Dependences Based on Empirical Data; Springer: New York, NY, USA, 1982.
72. Burges, C.J. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 1998, 2, 121-167.
73. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods; Cambridge University Press: Cambridge, UK, 2000.
74. Schölkopf, B.; Smola, A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond; MIT Press: Cambridge, MA, USA, 2001.
75. Schölkopf, B.; Smola, A.J.; Williamson, R.C.; Bartlett, P.L. New support vector algorithms. Neural Comput. 2000, 12, 1207-1245.
76. Steger, C.; Ulrich, M.;Wiedemann, C. Machine Vision Algorithms and Applications; WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2008; Chapter 3.
77. Haralick, R.M.; Shanmugam, K.; Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 3, 610-621.
78. Ballard, D.H. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 1981, 13, 111-122.
79. Ulrich, M.; Steger, C.; Baumgartner, A. Real-time object recognition using a modified generalized Hough transform. Pattern Recogn. 2003, 36, 2557-2570.
80. Steger, C. Similarity measures for occlusion, clutter, and illumination invariant object recognition. Pattern Recogn. 2001, 34, 148-154.
81. Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, PAMI-8, 679-698.
82. Burg, H.; Moser, A.; Eds. Handbuch Verkehrsunfallrekonstruktion; Vieweg+Teubner Verlag/GWV Fachverlage GmbH: Wiesbaden, Germany, 2009.
83. Economic and Social Council. European Agreement on Main International Traffic Arteries; Technical Report; Economic and Social Council: Geneva, Switzerland, 2002.
84. Spacek, P.; Belopitov, I. Geschwindigkeiten in Kurven; Technical Report Nr. 420; Society for Research and Standardization in the Field of Road and Transportation: Zurich, Switzerland, 1999.
85. Hinz, S.; Kurz, F.; Weihing, D.; Meyer, F.; Bamler, R. Evaluation of traffic monitoring based on spatio-temporal co-registration of SAR data and optical image sequences. Photogramm. Fernerkund. Geoinf. 2007, 2007, 309-325.
86. Bertini, R.L.; El-Geneidy, A.; Tantiyanugulchai, S. Toward automation of mobility measures in Portland, Oregon: Microscopic validation of archived freeway sensor data. In Proceedings of the ITS America 2004, 14th Annual Meeting and Exposition: At the Crossroads: Integrating Mobility Safety and Security, San Antonio, TX, USA, 26-28 April 2004.
87. Leonhardt, A. Detektortest Siemens LD4; Technical Report; TU München-Lehrstuhl für Verkehrstechnik: Munich, Germany, 2004.
88. Nihan, N.L.; Zhang, X.; Wang, Y. Evaluation of Dual-Loop Data Accuracy Using Video Ground Truth Data: Dual Loop Error Technical Report, National Technical Information Service: Springfield, VA, USA, 2002.