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A Semi-Automated Method for Monitoring of Roads with Traffic from High Resolution Satellite Image


Affiliations
1 Student, Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu - 603 203, India
2 Associate Professor, Department of Civil Engineering, SRM Institute of Science and Technology, Kanchipuram, Tamil Nadu - 603 203, India

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Efficient traffic management is critical in today’s overcrowded urban environments. Thanks to modern satellite imagery, high resolution images of current traffic conditions at regular intervals are easily available. In order to be able to respond to changes in traffic conditions quickly, it is critical to have an efficient and accurate system for traffic analysis of the static imagery available from these high resolution satellites. Two sub-problems are involved : detection of roads and detection of vehicles. A variety of algorithms are already available for solving these problems individually. For road tracking, we have median filers, color box segmentation, morphological processing, etc. There are algorithms like Bayesian background transformation, Blob extraction, and other vision based algorithms for vehicle detection. The goal of this paper is to automate tracking of roads and vehicles together from high resolution satellite images at any time statically, that is, without motion detection and leaving areas which are not roads like parking lot, settlements etc. Doing so, key points can be formalized in urban planning like finding other routes, human health factors, construction in endangered areas etc. By achieving the targeted objective and comparison with different algorithms, many problem statements can be defined and can be worked upon towards developing a solution.

Keywords

Detection, High Resolution Satellite Images, Roads, Traffic Management, Vehicles, Tracking.

Manuscript Received : October 11, 2020 ; Revised : March 12, 2021 ; Accepted : March 14, 2021. Date of Publication : June 5, 2021.

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  • S. Jaiswal and A. S. Bhaskar, “Developing an algorithm for tracking vehicles in high resolution satellite image and aerial photographs,” Int. J. of Eng. Res. & Technol. Geospatial, vol. 4, no. 20, 2016.

  • Comput. vision. In Wikipedia. [Online]. Available: en.wikipedia.org/wiki/Computer_vision

  • Homepage. [Online]. Available: inf.ed.ac.uk/rbf/HIPR2/median.htm

  • S. Srivastava, R. Singal, and M. Lumb, “Efficient lane detection algorithm using different filtering,” Int. J. of Comput. Appl. (0975 – 8887), vol. 88, no. 3, pp. 6 – 11, February 2014. [Online]. Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.428.6669&rep=rep1&type=pdf

  • T. Zhao, and R. Nevatia, “Car detection in low resolution aerial images,” Image and Vision Computing, vol. 21, no. 8, pp. 693–703, 2003. doi: https://doi.org/10.1016/S0262-8856(03)00064-7

  • “Blob Detection”. Scikit-image. Image Proc. in Python.[Online]. Available: Scikit-image.org/docs/dev/auto_examples/features_detection/plot_blob.html

  • “Color Thresholder.” MathWorks. .[Online]. Available: https://in.mathworks.com/help/images/ref/colorthresholder-app.html?s_tid=srchtitleIn.mathworks.com/help/matlab/ref/gradient.html

  • “The gradient and directional derivative,” Web Study Guide for Vector Calculus. [Online]. Available: http://sites.science.oregonstate.edu/math/home/programs/undergrad/CalculusQuestStudyGuides/vcalc/grad/grad.html

  • M. R. McCord, C. J. Merry, and P. Goel, “Incorporating satellite imagery in traffic monitoring programs,” North Americ. Travel Monitoring Exhibition and Conf. Charlotte, 1998.

  • X. Li, X. Yao, Y. L. Murphey, R. Karlsen, and G. Gerhart, “A real-time vehicle detection and tracking system in outdoor traffic scenes,” Proc. of the 17th Int. Conf. on Pattern Recognition, pp. 761–764, ICPR 2004, Cambridge, 2004. doi: https://10.1109/ICPR.2004.1334370

  • S. Maldonado-Bascon, S. Lafuente-Arroyo, P. Gil-Jimenez, H. Gomez-Moreno, and F. Lopez-Ferreras, "Road-sign detection and recognition based on support vector machines," in IEEE Trans. on Intelligent Transportation Syst., vol. 8, no. 2, pp. 264–278, June 2007. doi: https://doi.org/ 10.1109/TITS.2007.895311.

  • Y. Jung, and Y. Ho, "Multiple-object tracking under occlusion conditions," In Proc. SPIE 4067, Visual Commun. and Image Proc., May 30, 2000, Perth, Australia. The Int. Soc. for Opt. Eng., 2000. doi: https://doi.org/10.1117/12.386706.

  • S. Larsen, H. Koren, and R. Solberg, “Traffic monitoring using very high resolution satellite imagery, photogrammetric engineering and remote sensing,” Photogrammetric Enginering & Remote Sensing, vol. 75, no. 7, pp. 859–869, 2009.

  • B. Placzek, “A real time vehicle detection algorithm for vision-based sensors”. In: Bolc L., Tadeusiewicz R., Chmielewski L.J., Wojciechowski K. (eds.) Comput. Vision and Graph. ICCVG 2010. Lecture Notes in Comput. Sci., vol. 6375. Springer, Berlin, Heidelberg. doi: https://doi.org/10.1007/978-3-642-15907-7_26

  • S. Ghaffarian, and I. Gokasar, “Traffic density measurement by automatic detection of vehicles using gradient vectors from aerial images,” Int. J. of Civil and Environmental Eng., vol. 9, no. 8, 2015. doi: https://doi.org/10.5281/zenodo.1107513

  • P. Jaikumar, A. Singh, and S. K. Mitra, “Background subtraction in videos using Bayesian learning with motion information,” Proceeding of the Brit. Mach. Vision Conf., 2008. doi: https://doi.org/10.5244/C.22.61

  • Y. Kuo, N. Pai, and Y. Li, “Vision-based vehicle detection for a driver assistance system,” Comput. and Mathematics with Appl., vol. 61, no. 8, pp. 2096–2100, 2011. doi: https://doi.org/10.1016/j.camwa.2010.08.081

  • R. A. Hadi, G. Sulong, and L. E. George, “Vehicle detection and tracking techniques: A concise rev.”, Signal & Image Proc.: An Int. J., vol. 5, no. 1, 2014. doi: https://doi.org/10.5121/sipij.2013.5101

  • V. H. Mistry, and R. Makwana, “Survey: Vision based road detection techniques,” Int. J. of Comput. Sci. and Inform. Technologies, vol. 5, no. 3, pp. 4741–4747, 2014.

  • R. Radha and C. P. Sumathi, “A novel approach to extract text from license plate of vehicles,” Signal & Image Proc. An Int. J., vol. 3, no. 4, pp. 181–192, 2012.

  • K. Somasundaram and T. Genish, “Binarization of MRI with intensity inhomogeneity using K-Means clustering for segmenting Hippocampus”. The Int. J. of Multimedia & Its Appl., vol. 5, no. 1, 11–19, 2013. doi: https://doi.org/10.5121/ijma.2013.5102

  • R. Diwate, “Study of different algorithms for pattern matching,” Int. J. of Advanced Res. in Comput. Sci. and Software Eng., vol. 3, no. 3, 2013.

  • N. Sinkovics, “Pattern matching in qualitative analysis,” In C. Cassell, A. L. Cunliffe, and G. Grandy, The Sage Handbook of Qualitative Bus. and Manage. Res. Methods, pp. 468–484. London: SAGE Publications Ltd. doi: https://doi.org/10.4135/9781526430236

  • U. Stanczyk, “Feature evaluation by filter, wrapper, and embedded approaches,” Stud. in Computational Intell., vol. 584, pp. 29–44, 2015. [Online]. Available: https://link.springer.com/chapter/10.1007%2F978-3-662-45620-0_3

  • S. Banerjee, T. Chakrabarti, and D. Sinha, “A genetic algorithm based pattern matcher,” Int. J. of Scientific & Eng. Res., vol. 3, no. 11, 2012. [Online]. Available: https://www.ijser.org/paper/A-Genetic-Algorithm-Based-Pattern-Matcher.html

  • Google Earth. [Online]. Available: earth.google.com/web. Accessed on: March 2, 2016.

  • ArcGIS Online. [Online]. Available: https://www.arcgis.com/index.html

  • “ENVI.” L3Harris Geospatial. [Online]. Available: https://www.harrisgeospatial.com/Software-Technology/ENVI

  • “MATLAB.” MathWorks. [Online]. Available: https://www.mathworks.com/products/matlab.html


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  • A Semi-Automated Method for Monitoring of Roads with Traffic from High Resolution Satellite Image

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Authors

Srishti Jaiswal
Student, Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu - 603 203, India
Aparna S. Bhaskar
Associate Professor, Department of Civil Engineering, SRM Institute of Science and Technology, Kanchipuram, Tamil Nadu - 603 203, India

Abstract


Efficient traffic management is critical in today’s overcrowded urban environments. Thanks to modern satellite imagery, high resolution images of current traffic conditions at regular intervals are easily available. In order to be able to respond to changes in traffic conditions quickly, it is critical to have an efficient and accurate system for traffic analysis of the static imagery available from these high resolution satellites. Two sub-problems are involved : detection of roads and detection of vehicles. A variety of algorithms are already available for solving these problems individually. For road tracking, we have median filers, color box segmentation, morphological processing, etc. There are algorithms like Bayesian background transformation, Blob extraction, and other vision based algorithms for vehicle detection. The goal of this paper is to automate tracking of roads and vehicles together from high resolution satellite images at any time statically, that is, without motion detection and leaving areas which are not roads like parking lot, settlements etc. Doing so, key points can be formalized in urban planning like finding other routes, human health factors, construction in endangered areas etc. By achieving the targeted objective and comparison with different algorithms, many problem statements can be defined and can be worked upon towards developing a solution.

Keywords


Detection, High Resolution Satellite Images, Roads, Traffic Management, Vehicles, Tracking.

Manuscript Received : October 11, 2020 ; Revised : March 12, 2021 ; Accepted : March 14, 2021. Date of Publication : June 5, 2021.


References





DOI: https://doi.org/10.17010/ijce%2F2021%2Fv4i1%2F160806