Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

Sayed M.M. Kahaki; Md. Jan Nordin; Amir H. Ashtari

doi:10.5614/itbj.ict.2012.6.2.4

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

Published: 2012-01-01 | DOI: 10.5614/itbj.ict.2012.6.2.4 | Vol 6, Issue 2 (2012) | Cited by: 8

Authors

Sayed M.M. Kahaki

Department of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia

ORCID: 0000-0002-0249-3238 · OpenAlex: A5086762117

Md. Jan Nordin

Department of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia

ORCID: 0000-0002-7524-5665 · OpenAlex: A5033249451

Amir H. Ashtari

Department of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia

ORCID: 0000-0002-1022-8434 · OpenAlex: A5049817112

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Abstract

One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.

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Research Intelligence

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FWCIfield-weighted

84th

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Open Access

Topics

Automated Road and Building Extraction Primary

1.00

Ocean Engineering › Engineering › Physical Sciences

Remote Sensing and LiDAR Applications

1.00

Advanced Image Fusion Techniques

1.00

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Citation Timeline

Year	Citations
2022	2
2021	1
2020	1
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Semantic Profile AI-classified research signals

Core Domains

Bottleneck 0.85

level 2

Computer science 0.73

level 0

Constant false alarm rate 0.64

level 2

Secondary Topics

Traffic congestion 0.53 ALARM 0.49 Traffic flow (computer networking) 0.48 Real-time computing 0.46 Artificial intelligence 0.43 Remote sensing 0.43 Mode (computer interface) 0.41 Computer vision 0.34

Institution Network

National University of Malaysia MY
Seyed Mostafa Mousavi Kahaki · Md Jan Nordin · Amir Hossein Ashtari

References

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Chen, S., Wang, W. & Zuylen, H.V., Construct Support Vector Machine Ensemble to Detect Traffic Incident, Expert System with Applications, Elsevier, ISSN 0957-4174, PP. 10976-10986, DOI: 10.1016/ j.eswa.2009.02.039, 2009.
Wen, H., Yang, Z., Jiang G., & Shao, C., A New Algorithm of Incident Detection on Freeways, Vehicle Electronics Conference, IVEC 2001. Proceedings of the IEEE International, pp.197-202, DOI: 10.1109/ IVEC.2001.961753, 2001.
Chen, S.Y. & Wang, W., Decision Tree Learning for Freeway Automatic Incident Detection, Expert System with Applications, Elsevier, ISSN 0957-4174, pp. 4101-4105, DOI: 10.1016/j.eswa.2008.03.012, 2008. DOI: 10.1016/j.eswa.2008.03.012
Masters, P.H., Lam, J.K. & Wong, K., Incident Detection Algorithms for COMPASS-An Advanced Traffic Management System, Vehicle Navigation and Information Systems Conference, 2, pp. 295- 310, DOI: 10.1109/VNIS.1991.205776, 1991. DOI: 10.1109/vnis.1991.205776
Sujeeva A.W., An Application of Artificial Neural Networks in Freeway Incident Detection, PhD. Dissertation, University of South Florida, 1998.
Skorput, P., Mandzuka, S. & Jelusic, N., Real-Time Detection of Road Traffic Incidents, Promet-Traffic & Transportation, 22, pp. 273-283, 2010.
Moynihan, G.P., Fonseca, D.J., Brumback, T. & Fernandes, H., Evacuation Decision Support System for Road Incident Detection and Characterization, Journal of Homeland Security and Emergency Management 6, 2009.
Durduran, S.S., A Decision Making System to Automatic Recognize of Traffic Accidents on The Basis of A GIS Platform, Expert System with Application, pp. 7729-7736, 2010.
Jin, H., Feng, Y.M. & Li, Z.R., Extraction of Road Lanes from HighResolution Stereo Aerial Imagery Based on Maximum Likelihood Segmentation And Texture Enhancement, Digital Image Computing: Techniques and Applications, pp. 271-276, DOI: 10.1109/DICTA. 2009.52, 2009. DOI: 10.1109/dicta
Lin, Y.C. & Saripalli, S., Road Detection and Tracking from Aerial Desert Imagery, Journal of Intelligent & Robotic Systems 65, pp. 345-359, DOI: 10.1007/s10846-011-9600-6, 2012. DOI: 10.1007/s10846-011-9600-6
Sahli, S., Ouyang, Y., Sheng, Y.L. & Lavigne, D.A., Robust Vehicle Detection in Low-Resolution Aerial Imagery, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications, DOI: 10.1117/12.850387, 2010. DOI: 10.1117/12.850387
Sahli, S., Duval, P.L., Sheng, Y.L. & Lavigne, D.A., Robust vehicle Detection in Aerial Images Based on Salient Region Selection and Superpixel Classification, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems And Applications, DOI: 10.1117/ 12.883890, 2011.
Kahaki, S.M.M. & Nordin, M.J. Highway Traffic Incident Detection Using High-Resolution Aerial Remote Sensing Imagery. Phys. Int., 1, pp. 110-114, DOI: 10.3844/pisp.2010.110.114, 2010. DOI: 10.3844/pisp.2010.110.114
Higashikubo, M., Hinenoya, T. & Takeuchi, K., Queue Length Measurement Using an Image Processing Sensor, Universal Traffic Management Society of Japan, 2005.
Juozapavicius, A., Blake, R. & Kazimianec, M., Image Processing in Road Traffic Analysis, Nonlinear Analysis: Modeling and Control, Lithuanian Association of Nonlinear Analysts Journal, 10(4), pp. 315-332, 2005.
Ho, L. W. & Kleeman, L., Real Time Object Tracking using Reflectional Symmetry and Motion, IEEE, pp. 2798-2803, Oct 2006.
Hirzallah, N., Automated Camera Monitoring System for Selective Areas of Interest, J. Comput. Sci., 3, pp. 62-66. DOI: 10.3844/jcssp.2007.62.66, 2007. DOI: 10.3844/jcssp.2007.62.66
Panich, S., Method of Object Detection for Mobile Robot, J. Comput. Sci., 6, pp. 1151-1153. DOI: 10.3844/jcssp.2010.1151.1153, 2010. DOI: 10.3844/jcssp.2010.1151.1153
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Hinz, S., Automatic Road Extraction in Urban Scenes-and Beyond, International Archives of Photogrammetry and Remote Sensing, 2005.
Zhenfeng, Z., Hanqing, L., Hu, J. & Keiichi, U., Car Detection Based on Multi-Cues Integration, IEEE, 2004. DOI: 0-7695-2128-2/04.
Kahaki, S.M.M. & Nordin, M.J., Vision-Based Automatic Incident Detection System Using Image Sequences for Intersections, Pattern Analysis and Intelligent Robotics (ICPAIR), pp. 3-7, 2011. DOI: 10.1109/ ICPAIR.2011.5976902.
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Gonzalez, R.C., Woods, R.E. & Eddins, S.L., Digital Image Processing Using MATLAB, 1st ed., Prentice Hall, pp. 102-105, September 5, 2003.
Oriol, R.T. & Valveny, E., Radon Transform for Lineal Symbol Representation, IEEE, icdar, 1, pp.195, 2003.
Beylkin, G., Discrete Radon Transform, IEEE Transactions on Acoustics, Speech and Signal Processing, 35(2), pp. 162-172, 1987.
Sanz, J.L.C., Hinkle, E.B. & Jain, A.K., Radon and Projection Transform-Based Computer Vision, Springer-Verlag, pp. 132-138, 1987.
Jafari-Khouzani, K. & Soltanian-Zadeh, H., Radon Transform Orientation Estimation For Rotation Invariant Texture Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), pp. 1004-1008, June 2005.
Zeller, K., Hinz, S., Rosenbaum, D., Leitloff, J. & Reinartz, P. Traffic Monitoring without Single Car Detection from Optical Airbone Images, ISPRS Hannover Workshop, July 2009.
Reinartz, P., Lachaise, M., Schmeer, E., Krau, T. & Runge, H., Traffic Monitoring with Serial Images from Airborne Cameras, ISPRS Journal of Photogrammetry and Remote Sensing, 61, pp. 149-158, 2006.
Rosenbaum, D., Kurz, F., Thomas, U., Suri, S. & Reinartz, P., Towards Automatic Near Real-Time Traffic Monitoring with An Airborne Wide Angle Camera System, European Transport Research Review, 1, Springer, ISSN 1867-0717, pp. 11-21, 2009.
Coifman, B., Beymer, D., McLauchlan, P. & Malik, J., A Real-Time Computer Vision System for Vehicle Tracking and Traffic Surveillance, Transportation Research Part C: Emerging Technologies, 6(4), pp. 271-288, 1998.
Cassidy, M.J. & Bertini, R.L., Some Traffic Features at Freeway Bottlenecks, Transportation Research Part B: Methodological, 33(1), pp. 25-42, 1999.
Bertini, R.L. & Cassidy, M.J., Some Observed Queue Discharge Features at A Freeway Bottleneck Downstream of A Merge, Transportation Research Part A: Policy and Practice, 36(8), pp. 683-697, 2002.
Chung, K., Rudjanakanoknad, J. & Cassidy, M.J., Relation between Traffic Density and Capacity Drop at Three Freeway Bottlenecks, Transportation Research Part B: Methodological, 41(1), pp. 82-95, 2007.

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[ref-1] Lu, J., Chen, S., Wang, W. & Zuylen, H.V., A Hybrid Model of Partial Least Squares and Neural Network for Traffic Incident Detection, Expert Systems with Applications, ISSN 0957-4174, pp. 4775-4784, DOI: 10.1016/j.eswa.2011.09.158, 2011. DOI: 10.1016/j.eswa.2011.09.158

[ref-2] Chen, S., Wang, W. & Zuylen, H.V., Construct Support Vector Machine Ensemble to Detect Traffic Incident, Expert System with Applications, Elsevier, ISSN 0957-4174, PP. 10976-10986, DOI: 10.1016/ j.eswa.2009.02.039, 2009.

[ref-3] Wen, H., Yang, Z., Jiang G., & Shao, C., A New Algorithm of Incident Detection on Freeways, Vehicle Electronics Conference, IVEC 2001. Proceedings of the IEEE International, pp.197-202, DOI: 10.1109/ IVEC.2001.961753, 2001.

[ref-4] Chen, S.Y. & Wang, W., Decision Tree Learning for Freeway Automatic Incident Detection, Expert System with Applications, Elsevier, ISSN 0957-4174, pp. 4101-4105, DOI: 10.1016/j.eswa.2008.03.012, 2008. DOI: 10.1016/j.eswa.2008.03.012

[ref-5] Masters, P.H., Lam, J.K. & Wong, K., Incident Detection Algorithms for COMPASS-An Advanced Traffic Management System, Vehicle Navigation and Information Systems Conference, 2, pp. 295- 310, DOI: 10.1109/VNIS.1991.205776, 1991. DOI: 10.1109/vnis.1991.205776

[ref-6] Sujeeva A.W., An Application of Artificial Neural Networks in Freeway Incident Detection, PhD. Dissertation, University of South Florida, 1998.

[ref-7] Skorput, P., Mandzuka, S. & Jelusic, N., Real-Time Detection of Road Traffic Incidents, Promet-Traffic & Transportation, 22, pp. 273-283, 2010.

[ref-8] Moynihan, G.P., Fonseca, D.J., Brumback, T. & Fernandes, H., Evacuation Decision Support System for Road Incident Detection and Characterization, Journal of Homeland Security and Emergency Management 6, 2009.

[ref-9] Durduran, S.S., A Decision Making System to Automatic Recognize of Traffic Accidents on The Basis of A GIS Platform, Expert System with Application, pp. 7729-7736, 2010.

[ref-10] Jin, H., Feng, Y.M. & Li, Z.R., Extraction of Road Lanes from HighResolution Stereo Aerial Imagery Based on Maximum Likelihood Segmentation And Texture Enhancement, Digital Image Computing: Techniques and Applications, pp. 271-276, DOI: 10.1109/DICTA. 2009.52, 2009. DOI: 10.1109/dicta

[ref-11] Lin, Y.C. & Saripalli, S., Road Detection and Tracking from Aerial Desert Imagery, Journal of Intelligent & Robotic Systems 65, pp. 345-359, DOI: 10.1007/s10846-011-9600-6, 2012. DOI: 10.1007/s10846-011-9600-6

[ref-12] Sahli, S., Ouyang, Y., Sheng, Y.L. & Lavigne, D.A., Robust Vehicle Detection in Low-Resolution Aerial Imagery, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications, DOI: 10.1117/12.850387, 2010. DOI: 10.1117/12.850387

[ref-13] Sahli, S., Duval, P.L., Sheng, Y.L. & Lavigne, D.A., Robust vehicle Detection in Aerial Images Based on Salient Region Selection and Superpixel Classification, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems And Applications, DOI: 10.1117/ 12.883890, 2011.

[ref-14] Kahaki, S.M.M. & Nordin, M.J. Highway Traffic Incident Detection Using High-Resolution Aerial Remote Sensing Imagery. Phys. Int., 1, pp. 110-114, DOI: 10.3844/pisp.2010.110.114, 2010. DOI: 10.3844/pisp.2010.110.114

[ref-15] Higashikubo, M., Hinenoya, T. & Takeuchi, K., Queue Length Measurement Using an Image Processing Sensor, Universal Traffic Management Society of Japan, 2005.

[ref-16] Juozapavicius, A., Blake, R. & Kazimianec, M., Image Processing in Road Traffic Analysis, Nonlinear Analysis: Modeling and Control, Lithuanian Association of Nonlinear Analysts Journal, 10(4), pp. 315-332, 2005.

[ref-17] Ho, L. W. & Kleeman, L., Real Time Object Tracking using Reflectional Symmetry and Motion, IEEE, pp. 2798-2803, Oct 2006.

[ref-18] Hirzallah, N., Automated Camera Monitoring System for Selective Areas of Interest, J. Comput. Sci., 3, pp. 62-66. DOI: 10.3844/jcssp.2007.62.66, 2007. DOI: 10.3844/jcssp.2007.62.66

[ref-19] Panich, S., Method of Object Detection for Mobile Robot, J. Comput. Sci., 6, pp. 1151-1153. DOI: 10.3844/jcssp.2010.1151.1153, 2010. DOI: 10.3844/jcssp.2010.1151.1153

[ref-20] Lairong, C., Yuan, C. & Ronghua, J., Automatic Incident Detection Algorithm Based on Support Vector Machine. Natural Computation (ICNC), Sixth International Conference. DOI: 10.1109/ICNC.2010. 5583920, 2010. DOI: 10.1109/icnc.2010

[ref-21] Hong, Z.A. & Li, L., Artificial Immune Approach for Vehicle Detection from High Resolution Space Imagery, IJCSNS, 7(2), pp. 67-72, February 2007.

[ref-22] Hinz, S., Automatic Road Extraction in Urban Scenes-and Beyond, International Archives of Photogrammetry and Remote Sensing, 2005.

[ref-23] Zhenfeng, Z., Hanqing, L., Hu, J. & Keiichi, U., Car Detection Based on Multi-Cues Integration, IEEE, 2004. DOI: 0-7695-2128-2/04.

[ref-24] Kahaki, S.M.M. & Nordin, M.J., Vision-Based Automatic Incident Detection System Using Image Sequences for Intersections, Pattern Analysis and Intelligent Robotics (ICPAIR), pp. 3-7, 2011. DOI: 10.1109/ ICPAIR.2011.5976902.

[ref-25] Gonzalez, R.C. & Woods, R.E., Digital Image Processing, 2nd ed., New Jersey: Prentice Hall, pp. 68-70, 2002.

[ref-26] Gonzalez, R.C., Woods, R.E. & Eddins, S.L., Digital Image Processing Using MATLAB, 1st ed., Prentice Hall, pp. 102-105, September 5, 2003.

[ref-27] Oriol, R.T. & Valveny, E., Radon Transform for Lineal Symbol Representation, IEEE, icdar, 1, pp.195, 2003.

[ref-28] Beylkin, G., Discrete Radon Transform, IEEE Transactions on Acoustics, Speech and Signal Processing, 35(2), pp. 162-172, 1987.

[ref-29] Sanz, J.L.C., Hinkle, E.B. & Jain, A.K., Radon and Projection Transform-Based Computer Vision, Springer-Verlag, pp. 132-138, 1987.

[ref-30] Jafari-Khouzani, K. & Soltanian-Zadeh, H., Radon Transform Orientation Estimation For Rotation Invariant Texture Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), pp. 1004-1008, June 2005.

[ref-31] Zeller, K., Hinz, S., Rosenbaum, D., Leitloff, J. & Reinartz, P. Traffic Monitoring without Single Car Detection from Optical Airbone Images, ISPRS Hannover Workshop, July 2009.

[ref-32] Reinartz, P., Lachaise, M., Schmeer, E., Krau, T. & Runge, H., Traffic Monitoring with Serial Images from Airborne Cameras, ISPRS Journal of Photogrammetry and Remote Sensing, 61, pp. 149-158, 2006.

[ref-33] Rosenbaum, D., Kurz, F., Thomas, U., Suri, S. & Reinartz, P., Towards Automatic Near Real-Time Traffic Monitoring with An Airborne Wide Angle Camera System, European Transport Research Review, 1, Springer, ISSN 1867-0717, pp. 11-21, 2009.

[ref-34] Coifman, B., Beymer, D., McLauchlan, P. & Malik, J., A Real-Time Computer Vision System for Vehicle Tracking and Traffic Surveillance, Transportation Research Part C: Emerging Technologies, 6(4), pp. 271-288, 1998.

[ref-35] Cassidy, M.J. & Bertini, R.L., Some Traffic Features at Freeway Bottlenecks, Transportation Research Part B: Methodological, 33(1), pp. 25-42, 1999.

[ref-36] Bertini, R.L. & Cassidy, M.J., Some Observed Queue Discharge Features at A Freeway Bottleneck Downstream of A Merge, Transportation Research Part A: Policy and Practice, 36(8), pp. 683-697, 2002.

[ref-37] Chung, K., Rudjanakanoknad, J. & Cassidy, M.J., Relation between Traffic Density and Capacity Drop at Three Freeway Bottlenecks, Transportation Research Part B: Methodological, 41(1), pp. 82-95, 2007.