ОБЗОР МЕТОДОВ ПОИСКА И СОПРОВОЖДЕНИЯ ТРАНСПОРТНЫХ СРЕДСТВ НА ПОТОКЕ ВИДЕОДАННЫХ |
5 | |
2012 |
научная статья | 004.932 | ||
348-358 | компьютерное зрение, машинное обучение, поиск объектов на изображении, сопровождение объектов на видео, извлечение признаков, особые точки, детектор, дескриптор |
Приводится классификация методов детектирования транспортных средств на участке дорожной трассы. Рассматриваются преимущества и недостатки предлагаемых подходов. Описывается общая схема решения задачи с использованием методов компьютерного зрения. Рассматриваются методы поиска и последующего сопровождения объектов на потоке видеоданных. |
![]() |
1 . Traffic counting methods [http://people.hofstra.edu/ geotrans/eng/ch9en/meth9en/ch9m2en.html]. 2 . A summary of vehicle detection and Surveillance Technologies used in Intelligent Transportation Systems [http://www.fhwa.dot.gov/policyinformation/pubs/vdstits2007/vdstits2007.pdf]. 3 . Marsh Products, Inc. [http://www.marshproducts. com]. 4 . RAI Products [http://www.raiproducts.com/ vehicle-detection-systems.html]. 5 . International Road Dynamics Inc. [http://www.ir-dinc.com/products/sensors_accessories/on_road_sensors/]. 6 . Vaxtor Systems [www.vaxtor.com]. 7 . Архив новостей по рубрике Digital Signal Processing [http://www.compeljournal.ru/enews/rubric/ dsp]. 8 . Технология DaVinci – новая эра в цифровой обработке видеосигнала [http://www.compeljournal.ru/ images/articles/2005_10_4.pdf]. 9 . Quartics Products (integrated circuits for advanced video digital video processing) [http://www.quar-tics.com/products.html]. 10 . Application-Specific Integrated Circuit [http:// www.siliconfareast.com/asic.htm]. 11 . Arth C., Limberger F., Bischof H. Real-Time License Plate Recognition on an Embedded DSP-Platform // Proceedings of the Conference on Computer Vision and Pattern Recognition. 2007. 12 . Hirose K., Torio T., Hama H. Robust Extraction of Wheel Region for Vehicle Position Estimation using a Circular Fisheye Camera // International Journal of Computer Science and Network Security. 2009. V. 9. №12. 13 . Sivaraman S., Trivedi M.M. A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking //IEEE Transactions on Intelligent Transportation Systems. 2010. V.11. № 2. P. 267–276. 14 . Kim Z.W., Malik J. Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking // Proceedings of the ICCV’03. 2003. V. 1. P. 524–531. 15 . Tsai Y.M., Tsai C.C., Huang K.Y., Chen L.G. An intelligent vision-based vehicle detection and tracking system for automotive applications // Proceedings of the IEEE International Conference on Consumer Electronics. 2011. P. 113–114. 16 . Amit Y. 2D Object Detection and Recognition: models, algorithms and networks. The MIT Press, 2002. 325 p. 17 . Shotton J., Blake A., Cipolla R. Contour-based Learning for Object Detection // Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV’05). 2005. V.1. P. 503–510. 18 . Torralba A., Murphy K.P., Freeman W.T., Rubin M.A. Contex-based Vision System for Place and Object Recognition // Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV’03). 2003. V. 1. P. 273–283. 19 . Myung Jin Choi, Lim J.J., Torralba A., Willsky A.S. Exploiting Hierarchical Contex on a Large Database of Object Categories // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’10). 2010. P. 129–136. 20 . Felzenszwalb P.F., Girshick R.B., McAllester D., Ramanan D. Object Detection with Discriminatively Trained Part Based Models // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. V. 32. №9. P. 1627–1645. 21 . Druzhkov P.N., Eruhimov V.L., Kozinov E.A., et al. On some new object detection features in OpenCV library // Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications. 2011. V. 21. №3. P. 384–386. 22 . Viola P., Jones M.J. Robust Real-Time Face Detection // International Journal of Computer Vision. 2004. №57(2). P. 137–154. 23 . Viola P., Jones M.J. Rapid object detection using a boosted cascade of simple features // Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition. 2001. 24 . Pentland A., Choudhury T. Face Recognition for Smart Environments // IEEE Computer Vision. 2000. P. 50–55. 25 . Alonso D., Saldaro L., Nieto M. Robust Vehicle Detection through Multidimensional Classification for on Broad Video Based Systems // IEEE. 2007. 26 . Dalal N., Triggs B. Histograms of oriented gradients for human detection // Proceedings of the CVPR’05. 2005. 27 . Viola P., Jones M.J., Snow D. Detecting pedestrians using patterns of motion and appearance // Proceedings of the 9th International Conference on Computer Vision (ICCV’03). 2003. V. 1. P. 734–741. 28 . Gavrila D.M., Giebel J., Munder S. Vision-based pedestrian detection: the protector system // Proceedings of the IEEE Intelligent Vehicles Symposium, Parma, Italy. 2004. P. 13–18. 29 . Hilario C., Collado J.M., Armingol J.M., Escalera A. Pyramidal Image Analysis for Vehicle Detection // Proceedings to Intelligent Vehicles Symposium. 2005. P. 88–93. 30 . Szeliski R. Computer Vision: Algorithms and Applications. Springler, 2010. 979 p. 31 . Форсайт Д., Понс Ж. Компьютерное зрение. Современный подход. М.: Изд. дом «Вильямс», 2004. 465 с. 32 . Bradski G., Kaehler A. Learning OpenCV Computer Vision with OpenCV Library. O' Reilly Media Publishers, 2008. 571p. 33 . Sonka M., Hlavac V., Boyle R. Image Processing, Analysis and Machine Vision. Thomson, 2008. 866 p. 34 . Leibe B., Leonardis A., Schiele B. Robust Object Detection with Interleaved Object Categoization and Segmentation. Springler Science + Business Media, LLC, 2007. 35 . Lee P.H., Chiu T.H., Lin Y.L., Hung Y.P. Real-time pedestrian and vehicle detection in video using 3D cues // Proceedings of the 2009 IEEE international conference on Multimedia and Expo (ICME’09). 2009. P. 614–617. 36 . Horn B., Schunk B. Determing Optical Flow // MIT Artificial Intelligence Laboratory. 1980. №572. 37 . Wang J.Y.A., Adelson E.H. Representing moving images with layers // IEEE Transactions on Image Processing. 1994. 3(5). P. 625–638. 38 . Kumar M.P., Torr P.H.S., Zisserman A. Learning Layered Motion Segmentations of Video // International Journal of Computer Vision (IJCV). 2008. V.76, №3. P. 311–319. 39 . Yilmaz A., Javed O., Shah M. Object tracking: A survey // ACM Computing Surveys. 2006. V. 38, № 4. Article 13. 40 . Veenman C., Reinders M., Backer E. Resolving motion correspondence for densely moving points // IEEE Trans. Pattern Analysis Machine Intelligence. 2001. V.23, № 1. P. 54–72. 41 . Salarpour Amir, Salarpour Arezoo, Fathi M., Dezfoulian MirHossein Vehicle tracking using Kalman filter and features // Signal & Image Processing: An International Journal (SIPIJ). 2011. V. 2, №2. 42 . Dan S., Baojun Zh., Linbo T. A Tracking Algorithm Based on SIFT and Kalman Filter // Proceedings The 2nd International Conference on Computer Application and System Modeling. 2012. P. 1563–1566. 43 . Ning Li. Corner feature based object tracking using adaptive Kalman filter // Proceedings of the 9th International Conference on Signal Processing (ICSP 2008). 2008. P. 1432–1435. 44 . Isard M., Blake A. Condensation - conditional density propagation for visual tracking // Int. J. Comput. Vision. 1998. V. 29. №1. P. 5–28. 45 . Gustafsson F., Gunnarsson F., Bergman N. et al. Particle Filters for Positioning, Navigation and Tracking // IEEE Transactions on Signal Processing. 2002. Vol. 2. Is. 2. P. 425–437. 46 . Particle Filter Object Tracking [http://blogs.ore-gonstate.edu/hess/code/particles/]. 47 . Comaniciu D., Ramesh V., Meer P. Real-time tracking of non-rigid objects using mean shift // Proceedings of the CVPR’00. 2000. V. 2. P. 142–149. 48 . Exner D., Bruns E., Kurz D., Grundhofer A. Fast and robust CAMShift tracking // Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2010. P. 9–16. 49 . Formal Description of Moravec detector [http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm]. 50 . Tuytelaars T., Mikolajczyk K. Local Invariant Feature Detectors: A Survey // Foundation and Trends in Computer Vision. 2007. V. 3. №3. P. 177–280. 51 . Harris/Plessey Operator [http://kiwi.cs.dal.ca /~dparks/CornerDetection/harris.htm]. 52 . Mikolajczyk K., Schmid C. Scale and affine invariant interest point detectors // International Journal of Computer Vision. 2004. №60(1). P. 63–86. 53 . Matas J., Chum O., Urban M., Pajdla T. Robust wide baseline stereo from maximally stable extremal regions // British Machine Learning Conference. 2002. P. 384–393. 54 . Lindeberg T. Feature detection with automatic scale selection // International Journal of Computer Vision. 1998. V.30. Is. 2. 55 . Lowe D. Distinctive image features from scale-invariant keypoints // International Journal of Computer Vision. 2004. № 60. P. 91–110. 56 . Rosten E., Drummond T. Machine Learning for high-speed corner detection // Proceedings of the 9th European Conference on Computer Vision (ECCV’06). 2006. P. 430–443. 57 . Ke Y., Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors // Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR’04). 2004. V. 2. P. 506–513. 58 . Hastie T., Tibshirani R., Freidman J. The elements of statistical learning. Data mining, inference and prediction. 2001. 745 p. 59 . Bay H., Ess A., Tuytelaars T., Gool L.V. SURF: speed up robust features // Computer Vision and Image Understanding (CVIU). 2008. V. 110, № 3. P. 346–359. 60 . Tola E., Lepetit V., Fua P. A Fast Local Descriptor for Dense Matching // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’08). 2008. P. 1–8. 61 . Calonder M., Lepetit V., Strecha C., Fua P. BRIEF: Binary Robust Independent Elementary Features // Proceedings of the 11th European Conference on Computer Vision (ECCV’10). 2010. 62 . Rublee E., Rabaud V., Konolige K., Bradski G. ORB: an efficient alternative to SIFT or SURF // Proceedings of the International Conference on Computer Vision (CVPR’11). 2011. P. 2564–2571. 63 . Mikolajczyk K., Schmid C. A Performance Evaluation of Local Descriptors // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005. V. 27, № 10. P. 1615–1630. 64 . Koen E.A., Gevers T., Snoek C.G.M. Evaluating color descriptors for object and scene recognition // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. V. 32, № 9. P. 1615–1630. 65 . Comparison of the OpenCV’s feature detection algorithms [http://computer-vision-talks.com/2011/01/ comparison-of-the-opencvs-feature-detection-algo-rithms-2/]. 66 . Gauglitz S., Holerer T., Turk M. Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking [http://cs.iupui.edu/~tuceryan/pdf-reposi-tory/Gauglitz2011.pdf]. 67 . Horn B.K.P., Schunck B.G. Determining optical flow // MIT, Artificial Intelligence Laboratory. 1980. 68 . Shi J., Tomasi C. Good features to track // IEEE. 1994. P. 593–600. 69 . Jin H., Favaro P., Soatto S. Real-time tracking and outlier rejection with changes in illumination // Proceedings of the ICCV’01. 2001. V. 1. P. 684–689. 70 . Barron J.L., Fleet D.J., Beauchemin S.S. Performance of optical flow techniques // International Journal of Computer Vision. 1994. V. 12. №1. P. 43–77. 71 . Kalal Z., Mikolajczyk K., Matas J. Forward-backward error: automatic detection of tracking failures // Proceedings of the ICPR’10. 2010. P. 2756–2759. 72 . Описание алгоритма Predator [http://robot-develop.org/archives/4463]. 73 . Cucchiara R., Grana C., Piccardi M., Prati A. Statistical and knowledge based moving object detection in traffic scene // Proceedings of the IEEE Int’l Con-ference on Intelligent Transportation Systems. 2000. P. 27–32. 74 . Cucchiara R., Grana C., Piccardi M. et al. Improving Shadow Suppression in Moving Object Detection with HSV Color Information // Proceedings of the IEEE International Conference on the Intelligent Transportation Systems. 2001. P. 334–339. 75 . Fung G.S.K., Yung N.H.C., Pang G.K.H., Lai A.H.S. Towards Detection of Moving Cast Shadows for Visual Traffic Surveillance // Systems, Man, and Cybernetics. 2001. V. 4. P. 2505–2510. 76 . Sanin A., Sanderson C., Lovell B.C. Shadow Detection: A Survey and Comparative Evaluation of Recent Methods // Pattern Recognition. 2012. V. 45, №4. P. 1684–1695. 77 . Arrospide J., Salgado L., Nieto M., Jaureguizar F. Robust Vehicle detection through multidimensional clas- sification for on broad video based systems // Proceedings of the ICIP’08. 2008. P. 2008–2011. 78 . Шапиро Л., Стокман Дж. Компьютерное зрение. М.: Бином. Лаборатория знаний, 2006. 752 с. 79 . Tamersoy B., Aggarwal J.K. Robust Vehicle Detection for Tracking in Highway Surveillance Videos using Unsupervised Learning // Advanced Video and Sig-nal Based Surveillance (AVSS '09). 2009. P. 529–534. 80 . Ballard D.H. Generalizing the Hough transform to detect arbitrary shapes // Readings in computer vision: issues, problems, principles and paradigms. San Francisco: Morgan Kaufmann Publishers Inc., 1987. P. 111–122. 81 . Sun Z., Bebis G., Miller R. On-road vehicle detection using Gabor filters and support vector machines // Digital Signal Processing. 2002. V. 2. P. 1019–1022. |