رديابي دقيق اشيای متحرک با استفاده از الگوريتمهاي Sift، KLT و DBSCAN
الموضوعات :عزیز کرمیانی 1 , عسگرعلی بویر 2
1 - دانشگاه شهید مدنی آذربایجان
2 - دانشگاه شهید مدنی آذربایجان
الکلمات المفتاحية: اشیای متحرک ردیابی DBSCAN KLT SIFT,
ملخص المقالة :
کشف و رديابي اشيای متحرک گامی اساسي در تجزيه و تحليل ويدئو ميباشد. در اين مقاله روشي جديد را براي رديابي همزمان چندين شيء متحرک در حوزه دید دوربین ثابت ارائه خواهيم کرد. در روش پيشنهادي مکان اشيای متحرک موجود در حوزه ديد دوربين را در هر مرحله و با استفاده از اطلاعات حرکت موجود بين دو فريم متوالي شامل فريم قبلي و فريم جاري از نظر زماني تعيين ميکنيم. در هر مرحله نقاط ویژگی Sift را روي فريم قبلي استخراج کرده و تناظر اين نقاط ويژگي را با استفاده از الگوريتم تناظريابي نقاط کلیدی KLT روي فريم جاري به دست ميآوريم. در ادامه و با در اختيار داشتن نقاط ويژگي متناظر بين دو فريم متوالي، اندازه حرکت نقاط ويژگي را محاسبه کرده و با حذف نقاط ويژگي با جابهجايي ثابت و يا ناچیز، نقاط ويژگي مرتبط به اشيای متحرک را کشف خواهيم کرد. سپس نقاط ويژگي برچسبگذاري شده به عنوان اشيای متحرک را با استفاده از الگوريتم خوشهبندي DBSCAN به خوشههاي مختلف به عنوان اشيای متحرک دستهبندي ميکنيم. با اين روش و در هر لحظه مکان تمامي اشيای متحرک موجود در حوزه ديد دوربين به دست آمده که با تناظريابي يک به يک بين اين اشيا و اشيای به دست آمده در فريم قبلي مکان جديد هر شيء را تعيين ميکنيم. نتايج روش پيشنهادي حاکي از دقت بالا و زمان مصرفي قابل قبول براي رديابي اشيای متحرک ميباشد. روش پیشنهادی دارای دقت 95% برای ردیابی اشیای متحرک بوده و در هر ثانیه 33 فریم را پردازش میکند که در مقایسه با روشهای معمول از نظر دقت و سرعت عملکرد مطلوبی دارد.
[1] A. Yilmaz, O. Javed, and M. Shah, "Object tracking: a survey," Acm Computing Surveys, vol. 38, no. 4, Article No. 13, 2006.
[2] J. K. Aggarwal and Q. Cai, "Human motion analysis: a review," Computer Vision and Image Understanding, vol. 73, no. 3, Mar 1999.
[3] I. Haritaoglu, D. Harwood, and L. S. Davis, "W 4: real-time surveillance of people and their activities," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, Aug 2000.
[4] I. S. Kim, H. S. Choi, K. M. Yi, J. Y. Choi, and S. G. Kong, "Intelligent visual surveillance-a survey," International J. of Control, Automation and Systems, vol. 8, no. 5, pp. 926-939, Sept. 2010.
[5] M. Piccardi, "Background subtraction techniques: a review," in Proc. IEEE International Conf. on Systems, Man and Cybernetics, pp. 3099-3104, Oct. 2004.
[6] R. Poppe, "Vision-based human motion analysis: an overview," Computer Vision and Image Understanding, vol. 108, no. 1, pp. 4-18, Feb. 2007.
[7] A. Karamiani and N. Farajzadeh, "Detecting and tracking moving objects in video sequences using moving edge features," in Proc. Scientific Cooperations International Workshops on Electrical and Computer Engineering Subfields, pp. 88-92, Aug. 2014.
[8] R. Zhang and J. Ding, "Object tracking and detecting based on adaptive background subtraction," Procedia Engineering, vol. 29, no. 1, pp. 1351-1355, Feb. 2012.
[9] J. Lim and W. Kim, "Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost algorithm," Multimedia Tools and Applications, vol. 65, no. 1, pp. 161-179, Jun. 2013.
[10] J. S. Lim and W. H. Kim, "Detection and tracking multiple pedestrians from a moving camera," Advances in Visual Computing, vol. 3804, pp. 527-534, Dec. 2005.
[11] K. Bowyer, C. Kranenburg, and S. Dougherty, "Edge detector evaluation using empirical ROC curves." in Proc. Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 354-359, Jun. 1999.
[12] J. Canny, "A computational approach to edge detection," Pattern Analysis and Machine Intelligence, IEEE Trans. on, vol. 8, no. 6, pp. 679-698, Nov. 1986.
[13] C. Harris and M. Stephens, "A combined corner and edge detector," in Proc. 4th Alvey Vision Conf., pp. 147-152, 31 Aug.-2 Sept. 1988.
[14] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International J. of Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004.
[15] H. P. Moravec, "Visual mapping by a robot rover," in Proc. of the 6th Int. Joint Conf. on Artificial Intelligence, vol. 1, pp. 598-600, Aug. 1979.
[16] J. Shi and C. Tomasi, "Good features to track," in Proc. IEEE Computer Society Conf. on, Computer Vision and Pattern Recognition, CVPR'94, vol. 10, pp. 593-600, Jun. 1994.
[17] L. Nanni, A. Lumini, and S. Brahnam, "Survey on LBP based texture descriptors for image classification," Expert Systems with Applications, vol. 39, no. 3, pp. 3634-3641, Feb. 2012.
[18] J. Ning, L. Zhang, D. Zhang, and C. Wu, "Robust object tracking using joint color-texture histogram," International J. of Pattern Recognition and Artificial Intelligence, vol. 23, no. 07, pp. 1245-1263, Nov. 2009.
[19] G. Paschos, "Perceptually uniform color spaces for color texture analysis: an empirical evaluation," IEEE Trans. on Image Processing, vol. 10, no. 6, pp. 932-937, Jun. 2001.
[20] L. Tao and G. Xu, "Color in machine vision and its application," Chinese Science Bulletin, vol. 46, no. 17, pp. 1411-1421, Feb. 2001.
[21] ع. کرمیانی، ن. فرجزاده و ح. خانی، "ردیابی دقیق اشیای متحرک بر اساس اطلاعات حرکت و الگوریتم k-means اتوماتیک،" بيستمين کنفرانس ملي سالانه انجمن کامپيوتر ايران، صص. 219-211، دانشگاه فردوسی مشهد، اسفند 1393.
[22] K. Chard and K. Bubendorfer, "A distributed economic meta-scheduler for the grid," in Proc. 8th IEEE Int. Symp. on Cluster Computing and the Grid, CCGRID'08, , pp. 542-547, Jun. 2008.
[23] L. Lu and S. Yang, "DIRSS-G: an intelligent resource scheduling system for grid environment based on dynamic pricing," International J. of Information Technology, vol. 12, no. 4, pp. 120-127, Jun. 2006.
[24] D. Abramson, R. Sosic, J. Giddy, and B. Hall, "Nimrod: a tool for performing parameterized simulations using distributed workstations," in Proc. 4th IEEE Symp. on High Performance Distributed Computing, pp. 112-121, Aug. 1995.
[25] B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in Proc. of the 7th Int. Joint Conf. on Artificial Intelligence, IJCAI'81, vol. 2, pp. 674-679, 24-28 Aug. 1981.
[26] M. Ester, H. P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proc. KDD, pp. 226-231, 1996.
[27] A. Milan, L. Leal-Taixé, I. Reid, S. Roth, and K. Schindler. "MOT16: A benchmark for multi-object tracking," arXiv preprint arXiv:1603.00831, 2016.
[28] S. He, Q. Yang, R. W. Lau, J. Wang, and M. H. Yang, "Visual tracking via locality sensitive histograms," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2427-2434, Jun. 2013.
[29] T. Zhao and R. Nevatia, "Tracking multiple humans in crowded environment," in Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR, vol. 2, pp. 406-413 , 27 Jun.-2 Jul. 2004.
[30] J. S. Lim and W. H. Kim, "Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost algorithm," Multimed Tools Appl, vol. 65, no. 1, pp. 161-179, Jun. 2013.
[31] R. Zhang and J. Ding, "Object tracking and detecting based on adaptive background subtraction," Procedia Engineering, vol. 29, pp. 1351-1355, 2012.