:: دوره 7، شماره 1 - ( 3-1398 ) ::
جلد 7 شماره 1 صفحات 23-1 برگشت به فهرست نسخه ها
حذف سایه خودروها در تصاویر ویدئویی با استفاده از ویژگی آنتروپی و فاصله اقلیدسی
علی کرمی* ، مسعود ورشوساز ، محسن سریانی ، محمد شکری
دانشگاه صنعتی خواجه نصیرالدین طوسی
چکیده:   (2722 مشاهده)
تشخیص حرکت خودرو در تصاویر ویدئویی به‌عنوان یک موضوع کلیدی در مباحث بینایی کامپیوتر محسوب میگردد. در سال‌های اخیر، روشهای گوناگونی به‌منظور استخراج خودروها پیشنهادشده است. یکی از مشکلات اصلی تشخیص خودروها در سیستمهای پردازش تصویر، سایه خودروها است. سایه خودروها به‌دلیل پیوستگی که با خود خودرو دارند، باعث تغییر ظاهر واقعی خودرو و همچنین اتصال خودروهای اطراف به یکدیگر میشوند. هدف اصلی در این تحقیق ارائه یک روش بهینه در زمینه حذف سایه خودروها با استفاده از ویژگی آنتروپی و فاصله اقلیدسی میباشد. با استفاده از ویژگیهای یاد شده، به هرکدام از پیکسلهای تصویر یک وزن اختصاص داده میشود. وزنی که به پیکسلهای مربوط به قسمت سایه و سطح آسفالت(پسزمینه) اختصاص داده میشوند خیلی نزدیک به هم هستند. زمانی که از عمل تفاضل پس‌زمینه استفاده میشود، سایهها به همراه پس‌زمینه استخراج و حذف خواهد شد. در این تحقیق از سه پایگاه داده جهت پیاده‌سازی و ارزیابی استفاده‌شده است. از شاخص‌های دقت کلی، نرخ تشخیص درست و اشتباه، و همچنین دقت تشخیص و ردیابی چند عارضهای برای نشان دادن دقت و صحت در شناسایی خودروها، استفاده شده است. با استفاده از این معیارها روش پیشنهادی با دو روش دیگر که در زمینه حذف سایه خودروها می‌باشند، مورد مقایسه قرار گرفته شد. نتایج نشان داد که روش پیشنهادی بسته به نوع شاخص بین 3 تا 12درصد دقت نتایج متغییر می‌باشد.
واژه‌های کلیدی: شناسایی خودرو، سایه، آنتروپی، فاصله اقلیدسی.
متن کامل [PDF 2202 kb]   (819 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: فتوگرامتری
دریافت: 1395/11/30 | پذیرش: 1396/4/3 | انتشار: 1398/3/31
فهرست منابع
1. [1] J. K. Aggarwal and Q. Cai, "Human motion analysis: A review," in Nonrigid and Articulated Motion Workshop, 1997. Proceedings., IEEE, 1997, pp. 90-102.
2. [2] A. Prati, I. Mikic, C. Grana, and M. M. Trivedi, "Shadow detection algorithms for traffic flow analysis: a comparative study," in Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE, 2001, pp. 340-345.
3. [3] J. Yoon, C. Koch, and T. J. Ellis, "ShadowFlash: an approach for shadow removal in an active illumination environment," in BMVC, 2002, pp. 1-10. [DOI:10.5244/C.16.62]
4. [4] J. Dai and D. Han, "Region-based moving shadow detection using affinity propagation," Int. J. Signal Process. Image Process. Pattern Recogn, vol. 8, pp. 65-74, 2015. [DOI:10.14257/ijsip.2015.8.3.06]
5. [5] A. Kar and K. Deb, "Moving cast shadow detection and removal from Video based on HSV color space," in Electrical Engineering and Information Communication Technology (ICEEICT), 2015 International Conference on, 2015, pp. 1-6. [DOI:10.1109/ICEEICT.2015.7307443]
6. [6] R. Zabihollahi and M. Soryani, "Vehicle Shadow Exclusion for a Vehicle Velocity Detection System," in IMECS, 2007, pp. 492-496.
7. [7] T. Matsuyama, T. Wada, H. Habe, and K. Tanahashi, "Background subtraction under varying illumination," Systems and Computers in Japan, vol. 37, pp. 77-88, 2006. [DOI:10.1002/scj.10166]
8. [8] R. Guo, Q. Dai, and D. Hoiem, "Single-image shadow detection and removal using paired regions," in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 2011, pp. 2033-2040. [DOI:10.1109/CVPR.2011.5995725]
9. [9] X. Liu, B. Dai, and H. He, "Real-time on-road vehicle detection combining specific shadow segmentation and SVM classification," in Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on, 2011, pp. 885-888. [DOI:10.1109/ICDMA.2011.219]
10. [10] N. Singh and A. Maxton, "A Survey on Shadow Detection Methods," IJARCET, Volume3, 2014.
11. [11] M. Qi, J. Dai, Q. Zhang, and J. Kong, "Cascaded cast shadow detection method in surveillance scenes," Optik-International Journal for Light and Electron Optics, vol. 125, pp. 1396-1400, 2014. [DOI:10.1016/j.ijleo.2013.08.006]
12. [12] A. Tiwari, P. K. Singh, and S. Amin, "A survey on Shadow Detection and Removal in images and video sequences," in Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference, 2016, pp. 518-523. [DOI:10.1109/CONFLUENCE.2016.7508175]
13. [13] J.-W. Hsieh, W.-F. Hu, C.-J. Chang, and Y.-S. Chen, "Shadow elimination for effective moving object detection by Gaussian shadow modeling," Image and Vision Computing, vol. 21, pp. 505-516, 2003. [DOI:10.1016/S0262-8856(03)00030-1]
14. [14] S. Nadimi and B. Bhanu, "Moving shadow detection using a physics-based approach," in Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2002, pp. 701-704.
15. [15] K. Onoguchi, "Shadow elimination method for moving object detection," in Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on, 1998, pp. 583-587.
16. [16] J. Stander, R. Mech, and J. Ostermann, "Detection of moving cast shadows for object segmentation," IEEE Transactions on multimedia, vol. 1, pp. 65-76, 1999. [DOI:10.1109/6046.748172]
17. [17] S. H. Khan, M. Bennamoun, F. Sohel, and R. Togneri, "Automatic shadow detection and removal from a single image," IEEE transactions on pattern analysis and machine intelligence, vol. 38, pp. 431-446, 2016. [DOI:10.1109/TPAMI.2015.2462355]
18. [18] L. Shen, T. Wee Chua, and K. Leman, "Shadow optimization from structured deep edge detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2067-2074. [DOI:10.1109/CVPR.2015.7298818]
19. [19] H. Song, B. Huang, and K. Zhang, "Shadow detection and reconstruction in high-resolution satellite images via morphological filtering and example-based learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, pp. 2545-2554, 2014. [DOI:10.1109/TGRS.2013.2262722]
20. [20] K.-L. Chung, Y.-R. Lin, and Y.-H. Huang, "Efficient shadow detection of color aerial images based on successive thresholding scheme," IEEE Transactions on Geoscience and Remote sensing, vol. 47, pp. 671-682, 2009. [DOI:10.1109/TGRS.2008.2004629]
21. [21] F. P. Luus, F. van den Bergh, and B. Maharaj, "Adaptive threshold-based shadow masking for across-date settlement classification of panchromatic QuickBird images," IEEE Geoscience and Remote Sensing Letters, vol. 11, pp. 1153-1157, 2014. [DOI:10.1109/LGRS.2013.2288428]
22. [22] J. C. S. Jacques, C. R. Jung, and S. R. Musse, "Background subtraction and shadow detection in grayscale video sequences," in XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05), 2005, pp. 189-196. [DOI:10.1109/SIBGRAPI.2005.15]
23. [23] J. S. Kulchandani and K. J. Dangarwala, "Moving object detection: Review of recent research trends," in Pervasive Computing (ICPC), 2015 International Conference on, 2015, pp. 1-5. [DOI:10.1109/PERVASIVE.2015.7087138]
24. [24] J.-M. Wang, Y.-C. Chung, C. Chang, and S.-W. Chen, "Shadow detection and removal for traffic images," in Networking, Sensing and Control, 2004 IEEE International Conference on, 2004, pp. 649-654.
25. [25] M. Golchin, F. Khalid, L. N. Abdullah, and S. H. Davarpanah, "Shadow detection using color and edge information," Journal of Computer Science, vol. 9, p. 1575, 2013. [DOI:10.3844/jcssp.2013.1575.1588]
26. [26] O. Tuzel, F. Porikli, and P. Meer, "A bayesian approach to background modeling," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)-Workshops, 2005, pp. 58-58.
27. [27] B. Chen, Y. Lei, and W. Li, "A novel background model for real-time vehicle detection," in Signal Processing, 2004. Proceedings. ICSP'04. 2004 7th International Conference on, 2004, pp. 1276-1279.
28. [28] W. Zhang, X. Z. Fang, and Y. Xu, "Detection of moving cast shadows using image orthogonal transform," in 18th International Conference on Pattern Recognition (ICPR'06), 2006, pp. 626-629. [DOI:10.1109/ICPR.2006.441]
29. [29] Y. Lu, H. Xin, J. Kong, B. Li, and Y. Wang, "Shadow removal based on shadow direction and shadow attributes," in 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06), 2006, pp. 37-37. [DOI:10.1109/CIMCA.2006.196]
30. [30] D.-m. Li, Y.-z. Wang, and B. Du, "Research on Segmentation Methods of Weed and Soil Background Under HSI Color Model," in Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on, 2009, pp. 628-631.
31. [31] T. Acharya and A. K. Ray, Image processing: principles and applications: John Wiley & Sons, 2005. [DOI:10.1002/0471745790]
32. [32] Y. G. Byun, Y. K. Han, and T. B. Chae, "A multispectral image segmentation approach for object-based image classification of high resolution satellite imagery," KSCE Journal of Civil Engineering, vol. 17, pp. 486-497, 2013. [DOI:10.1007/s12205-013-1800-0]
33. [33] K.-L. Chung, W.-J. Yang, and W.-M. Yan, "Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation," Journal of Visual Communication and Image Representation, vol. 19, pp. 299-310, 2008. [DOI:10.1016/j.jvcir.2008.02.002]
34. [34] J. Fan, G. Zeng, M. Body, and M.-S. Hacid, "Seeded region growing: an extensive and comparative study," Pattern recognition letters, vol. 26, pp. 1139-1156, 2005. [DOI:10.1016/j.patrec.2004.10.010]
35. [35] W. jian, "Study on Segmentation of Color Remote Sensing Image," Procedia Engineering, vol. 29, pp. 3312-3316, 2012. [DOI:10.1016/j.proeng.2012.01.486]
36. [36] K. López-de-Ipiña, J. Solé-Casals, M. Faundez-Zanuy, P. M. Calvo, E. Sesa, U. Martinez de Lizarduy, et al., "Selection of entropy based features for automatic analysis of essential tremor," Entropy, vol. 18, p. 184, 2016. [DOI:10.3390/e18050184]
37. [37] R. M. Haralick and K. Shanmugam, "Textural features for image classification," IEEE Transactions on systems, man, and cybernetics, vol. 3, pp. 610-621, 1973. [DOI:10.1109/TSMC.1973.4309314]
38. [38] M. Janalipour and M. Taleai, "Building change detection after earthquake using multi-criteria decision analysis based on extracted information from high spatial resolution satellite images," International Journal of Remote Sensing, vol. 38, pp. 82-99, 2017. [DOI:10.1080/01431161.2016.1259673]
39. [39] J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques: Elsevier, 2011.
40. [40] R. C. Gonzales and P. Wintz, Digital image processing (2nd ed.): Addison-Wesley Longman Publishing Co., Inc., 1987.
41. [41] Y.-H. Yang and M. D. Levine, "The background primal sketch: an approach for tracking moving objects," Machine Vision and applications, vol. 5, pp. 17-34, 1992. [DOI:10.1007/BF01213527]
42. [42] X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem," Information Sciences, vol. 340, pp. 250-261, 2016. [DOI:10.1016/j.ins.2016.01.033]
43. [43] A. Hakeem, K. Shafique, and M. Shah, "An object-based video coding framework for video sequences obtained from static cameras," in Proceedings of the 13th annual ACM international conference on Multimedia, 2005, pp. 608-617. [DOI:10.1145/1101149.1101289]
44. [44] K. Gupta and A. V. Kulkarni, "Implementation of an automated single camera object tracking system using frame differencing and dynamic template matching," in Advances in Computer and Information Sciences and Engineering, ed: Springer, 2008, pp. 245-250. [DOI:10.1007/978-1-4020-8741-7_44]
45. [45] R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, M. Boonstra, and V. Korzhova, "Performance evaluation protocol for face, person and vehicle detection & tracking in video analysis and content extraction (VACE-II)," Computer Science & Engineering University of South Florida, Tampa, 2006.



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دوره 7، شماره 1 - ( 3-1398 ) برگشت به فهرست نسخه ها