Traffic Density Measurement by Automatic Detection of Vehicles Using Gradient Vectors from Aerial Images
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.
[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.
[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.
@article{"International Journal of Architectural, Civil and Construction Sciences:70379", author = "Saman Ghaffarian and Ilgın Gökasar", title = "Traffic Density Measurement by Automatic Detection of Vehicles Using Gradient Vectors from Aerial Images", abstract = "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 = "Aerial images, intelligent transportation systems,
traffic density measurement, vehicle detection.", volume = "9", number = "8", pages = "965-5", }