Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31107
Traffic Density Measurement by Automatic Detection of Vehicles Using Gradient Vectors from Aerial Images

Authors: Saman Ghaffarian, Ilgın Gökasar


This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: Intelligent Transportation Systems, Vehicle Detection, aerial images, traffic density measurement

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2597


[1] P. Burlina, V. Parameswaran and R. Chellappa, “Sensitivity Analysis and Learning Strategies for Context-Based Vehicle Detection Algorithm,” in DARPA IU Workshop, College Park, MD, Center for automation Research, University of Maryland, 1997.
[2] H. Moon, R. Chellappa and A. Rosenfeld, “Performance Analysis of a Simple Vehicle Detection Algorithm,” Image and Computer Vision, Vol. 20, No. 1, pp. 1-13, 2002.
[3] K. Kozempel and R. Reulke, “Fast Vehicle Detection and Tracking in Aerial Image Bursts”, in ISPRS City Models, Roads and Traffic (CMRT) , Paris, France, Vol. 38, No. 3/W4, pp. 175-180, 2009.
[4] T. Zhao and R. Nevatia, “Car Detection in Low Resolution Aerial Image”, Image and Vision Computing, Vol. 21, No. 8, pp. 693-703, 2003.
[5] S. Hinz, “Detection of Vehicles and Vehicle Queues in High Resolution Aerial Images”, Pjotogrammetrie-Fernerkundung-Geoinformation (PFG), Vol. 3, No. 4, pp. 201-213, 2004.
[6] D. Lenhart, S. Hinz, J. Leitloff and U. Stilla, “Automatic Traffic Monitoring Based On Aerial Image Sequences”, Pattern Recognition and Image Analysis, Vol. 18, No. 3, pp. 400-405, 2008.
[7] J. Y. Choi and Y. K. Yang, “Vehicle Detection from Aerial Images Using Local Shape Information”, in Pacific Rim Symp. Advanced in Image and Video Technology (PSIVT) Berlin, Heidelberg, Germany, Springer-Verlag, pp. 227-236, 2009.
[8] A. C. Holt, E. Y. W. Seto, T. Rivard and G. Peng, “Object-Based Detection and Classification of Vehicles from High-Resolution Aerial Photography”, Photogrammetric Engineering and Remote Sensing (PE & RS), Vol. 75, No. 7, pp. 871-880, 2009.
[9] M. Elmiktay and T. Stathaki, “Car Detection in High-Resolution Urban Scenes Using Multiple Image Descriptors”, in Proc. Of International Conference on Pattern Recognition (ISPR), Stockholm, Sweden, pp. 4299-4304, 2014.
[10] X. Chen and Q. Meng, “Vehicle Detection from UAVs by Using SIFT with Implicit Shape Model”, in IEEE International Conference on Systems, Man, and Cybernetics, pp. 3139-3144, 2013.
[11] T. Moranduzzo and F. Melgani, “Detecting Cars in UAV Images with a Catalog-Based Approach,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 10, pp. 6356-6367, 2014.
[12] S. Tuermer, F. Kurz, P. Reinartz and U. Stilla, “Airborne Vehicle Detection in Dense Urban Areas Using HoG Features and Disparity maps,” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, Vol. 6, No. 6, pp. 2327-2337, 2013.
[13] H. Grabner, T. T. Nguyen B. Gruber and H. Bischof, “On-Line Boosting-Based Car Detection from Aerial Images”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 63, No. 3, pp. 382-396, 2008.
[14] S. Kluckner, G. Pacher, H. Garbner and H. Bischof, “A 3D Teacher for Car Detection in Aerial Images”, Image and Vision Computing, in ICCV, Rio de Janeiro, Brazil, 2007.
[15] J. Leitloff, S. Hinz and U. Stilla, “Vehicle Detection in Very High Resolution Satellite Images of City Area,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 7, pp. 2795-2806, 2010.
[16] J. Canny, “A Computational Approach to Edge Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6, pp. 697-698, 1986.
[17] S. Ghaffarian and S. Ghaffarian, “Automatic Building Detection Based On Purposive FastICA (PFICA) Algorithm Using Monocular High Resolution Google Earth Images,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 97, pp. 152-159, 2014.
[18] S. Ghaffarian and S. Ghaffarian, “Automatic Building Detection Based On Supervised Classification Using High Resolution Google Earth Images,” ISPRS Technical Commission III Symposium, Zurich, Switzerland, pp. 101-106, 2014.