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Rendering ground truth data sets to detect shadows cast by static objects in outdoors

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Abstract

In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically.

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References

  1. Albin S, Rougeron G, Péroche B, Trémeau A (2002) Quality image metrics for synthetic images based on perceptual color differences. IEEE Trans Image Process 11(9):961–971

    Article  Google Scholar 

  2. Blanco-Muriel M, Alarcón-Padilla D, López-Moratalla T, Lara-Coira M (2001) Computing the solar vector. Sol Energy 70(5):431–441

    Article  Google Scholar 

  3. Bouguet J (2009) Camera Calibration Toolbox for Matlab. URL:http://www.vision.caltech.edu/bouguetj/calib_doc

  4. Foresti G (1999) Object recognition and tracking for remote video surveillance. IEEE Trans Circuits Syst Video Techn 9(7):1045–1062

    Article  Google Scholar 

  5. Gibson, S, Cook J, Howard T, Hubbold R (2003) Rapid shadow generation in real-world lighting environments. In: Proceedings of the 14th Eurographics workshop on rendering, pp 219–229

  6. Grena R (2008) An algorithm for the computation of the solar position. Sol Energy 82(5):462–470

    Article  Google Scholar 

  7. Hsieh J, Hu W, Chang C, Chen Y (2003) Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image Vis Comput 21(6):505–516

    Article  Google Scholar 

  8. Kaneva B, Torralba A, Freeman WT (2011) Evaluation of image features using a photorealistic virtual world. In: IEEE international conference on computer vision, pp 2282–2289

  9. Martel-Brisson N, Zaccarin A (2005) Moving cast shadow detection from a Gaussian mixture shadow model. In: IEEE conference on computer vision and pattern recognition, vol 2, pp 643–648

  10. Matsushita Y, Nishino K, Ikeuchi K, Sakauchi M (2004) Illumination normalization with time-dependent intrinsic images for video surveillance. IEEE Trans Pattern Anal Mach Intell 26(10):1336–1347

    Article  Google Scholar 

  11. Mei X, Ling H, Jacobs DW (2009) Sparse representation of cast shadows via L1-regularized least squares. In: IEEE international conference on computer vision, pp 583–590

  12. Meister S, Kondermann D (2011) Real versus realistically rendered scenes for optical flow evaluation. In: IEEE Conference on Electronic Media Technology, pp 1–6

  13. Melamed ID, Green R, Turian JP (2003) Precision and recall of machine translation. In: Conference of the North American chapter of the association for computational linguistics on human language technology, vol 2, pp 505–516

  14. Mousazadeh H, Keyhani A, Javadi A, Mobli H, Abrinia K, Sharifi A (2009) A review of principle and sun-tracking methods for maximizing solar systems output. Renew Sustain Energy Rev 13(8):1800–1818

    Google Scholar 

  15. Nadimi S, Bhanu B (2004) Physical models for moving shadow and object detection in video. IEEE Trans Pattern Anal Mach Intell 26(8):1079–1087

    Article  Google Scholar 

  16. Prati A, Mikic I, Trivedi M, Cucchiara R (2003) Detecting moving shadows: algorithms and evaluation. IEEE Trans Pattern Anal Mach Intell 25(7):918–923

    Article  Google Scholar 

  17. Reda I, Andreas A (2004) Solar position algorithm for solar radiation applications. Sol Energy 76(5):577–589

    Article  Google Scholar 

  18. Roy V (2012) Sun Position. http://www.mathworks.com/matlabcentral/fileexchange/4605

  19. Salvador E, Cavallaro A, Ebrahimi T (2004) Cast shadow segmentation using invariant color features. Comput Vis Image Underst 95(2):238–259

    Article  Google Scholar 

  20. Santuari A, Lanz O, Brunelli R (2003) Synthetic movies for computer vision applications. In: International conference on visualization, imaging, and image processing, pp 1–6

  21. Stander J, Mech R, Ostermann J (1999) Detection of moving cast shadows for object segmentation. IEEE Trans Multimed 1(1):65–76

    Article  Google Scholar 

  22. Walraven R (1978) Calculating the position of the sun. Sol Energy 20(5):393–397

    Article  Google Scholar 

  23. Weiss Y (2001) Deriving intrinsic images from image sequences. In: IEEE international conference on computer vision, vol 2, pp 68–75

  24. Woodward A, Delmas P (2005) Synthetic ground truth for comparison of gradient field integration methods for human faces. In: Conference on image and vision computing New Zealand

  25. Xu D, Li X, Liu Z, Yuan Y (2005) Cast shadow detection in video segmentation. Pattern Recogn Lett 26(1):91–99

    Article  Google Scholar 

  26. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334

    Article  Google Scholar 

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Acknowledgement

The authors would like to thank Taylor Morris for many helpful comments to the manuscript.

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Correspondence to Cesar Isaza.

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This research was partially supported by IPN-SIP under grant contract 20121642.

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Isaza, C., Salas, J. & Raducanu, B. Rendering ground truth data sets to detect shadows cast by static objects in outdoors. Multimed Tools Appl 70, 557–571 (2014). https://doi.org/10.1007/s11042-013-1409-9

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  • DOI: https://doi.org/10.1007/s11042-013-1409-9

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