A System based on Adaptive Background Subtraction Approach for Moving Object Detection and Tracking in Videos

Öz

Video surveillance systems are based on video and image processing research areas in the scope of computer science. Video processing covers various methods which are used to browse the changes in existing scene for specific video. Nowadays, video processing is one of the important areas of computer science. Two-dimensional videos are used to apply various segmentation and object detection and tracking processes which exists in multimedia content-based indexing, information retrieval, visual and distributed cross-camera surveillance systems, people tracking, traffic tracking and similar applications. Background subtraction (BS) approach is a frequently used method for moving object detection and tracking. In the literature, there exist similar methods for this issue. In this research study, it is proposed to provide a more efficient method which is an addition to existing methods. According to model which is produced by using adaptive background subtraction (ABS), an object detection and tracking system’s software is implemented in computer environment. The performance of developed system is tested via experimental works with related video datasets. The experimental results and discussion are given in the study

Anahtar Kelimeler

• Background subtraction, object detection, object tracking, video processing

Videolardaki Haraketli Nesnelerin Tespit Ve Takibi İçin Uyarlanabilir Arkaplan Çıkarımı Yaklaşımı Tabanlı Bir Sistem

Öz

Gözetleme sistemleri temelinde bilgisayar bilimleri kapsamındaki video ve görüntü işleme araştırma alanları bulunmaktadır. Video işleme, belirli bir video görüntüsünde var olan sahne içerisindeki değişimleri incelemede kullanılabilecek çeşitli yöntemleri içermektedir. Günümüzde video işleme bilgisayar bilimlerinin en önemli araştırma alanlarından birisidir. İki-boyutlu videolar; çoklu ortam içeriktabanlı endekslemede, bilgi elde etmede, görsel gözetleme ve dağıtık çapraz-kamera ile gözetleme sistemlerinde, insan takibi, trafik izleme ve benzeri uygulamalardaki çeşitli bölütleme, nesne tespit ve takibinde kullanılmaktadır. Arkaplan çıkarımı (AÇ) yaklaşımı, hareketli nesne tespit ve takibi konusunda sıkça kullanılan yöntemlerden biridir. Literatürde bu konu ile ilgili benzer yöntemler de mevcuttur. Yapılan bu araştırma çalışmasında mevcut yöntemlere ek olarak daha etkin bir çözüme gidilmesi önerilmiştir. Uyarlanabilir arkaplan çıkarımı (UyAÇ) yaklaşımı kullanılarak oluşturulan modele göre bilgisayar ortamında nesne tespit ve takip sistemi yazılımı gerçekleştirilmiştir. İlgili video veri setleri ile deneysel çalışma yapılarak geliştirilen sistemin başarımı sınanmıştır. Deneysel sonuçlar ve tartışmaya çalışma içerisinde yer verilmektedir

Anahtar Kelimeler

• Arkaplan çıkarımı, nesne tespiti, nesne takibi, video işleme

Kaynakça

1. Barbieri, A.L., Arruda, G.F. de, Rodrigues, F.A., Bruno, O.M. ve Costa, L. da F. (2011) An entropy-based approach to automatic image segmentation of satellite images, Physica A: Statistical Mechanics and its Applications, 390(3), 512-518, ISSN 0378-4371, doi: 10.1016/j.physa.2010.10.015.
2. Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H. ve Rosenberger, C. (2008) Review and evaluation of commonly-implemented background subtraction algorithms, In: In 19th Int. Conf. of Pattern Recognition, (ICPR 2008), 1-4, doi: 10.1109/ICPR.2008.4760998.
3. Bennett, B., Magee, D.R., Cohn, A.G. ve Hogg, D.C. (2008) Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity, Image and Vision Computing, Cognitive Vision-Special Issue, 26(1), 67-81, doi:10.1016/j.imavis.2005.08.012.
4. Boult, T.E., Micheals, R., Gao, X., Lewis, P., Power, C., Yin, W. ve Erkan, A. (1999) Frame-Rate Omnidirectional Surveillance & Tracking of Camouflaged and Occluded Targets, In: In Proceedings of the Second IEEE Workshop on Visual Surveillance, VS. IEEE Computer Society, Washington, DC, USA, 48-55.
5. Bradski, G.R. ve Kaehler, A. (2008) Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly Media, Inc. Publication, 1005 Gravenstein Highway North, Sebastopol, CA 95472, 555 p., ISBN: 978-0-596-51613-0.
6. Brdiczka, O., Yuen, P., Zaidenberg, S., Reignier, P. ve Crowley, J.L. (2006) Automatic acquisition of context models and its application to video surveillance, In: In 18th Int. Conf. on Pattern Recognition (ICPR'06), Hong Kong, 1175-1178.
7. Camara-Chavez, G., Precioso, F., Cord, M., Phillip-Foliguet, S. ve Araujo, A.D.A. (2008) An interactive video content-based retrieval system. In: 15th Int. Conf. on Systems, Signals and Image Processing (IWSSIP 2008), 133-136, doi:10.1109/IWSSIP.2008.4604385.
8. Carmona, E.J., Martínez-Cantos, J. ve Mira, J. (2008) A new video segmentation method of moving objects based on blob-level knowledge, Pattern Recognition Letters, 29(3), 272- 285, doi:10.1016/j.patrec.2007.10.007.

9. CAVIAR, (2012) “Context aware vision using image-based active recognition”, http://homepages.inf.ed.ac.uk/rbf/CAVIAR, (Erişim tarihi: 07.Eylül.2012).
10. Cheung, S.-C. ve Kamath, C. (2004) Robust techniques for background subtraction in urban traffic video, Video Communications and Image Processing, SPIE Electronic Imaging, San Jose, UCRL-JC-153846-ABS, UCRL-CONF-200706,.
11. Cover, T.M., Thomas, J.A., Wiley, J. ve W. InterScience (1991) Elements of Information Theory, Wiley, New York.
12. Dickinson, P., Hunter, A. ve Appiah, K. (2009) A spatially distributed model for foreground segmentation, Image and Vision Computing, 27(9),1326–1335.
13. Erdem, C.E., Ernst, F., Redert, A. ve Hendriks, E. (2005) Temporal stabilization of video object tracking for 3D-TV applications, Signal Processing: Image Communication, 20,151- 167, doi: 10.1016/j.image.2004.10.005.
14. Foresti, G.L., Regazzoni, C.S. ve Varshney, P.K. (2003) Multisensor Surveillance Systems: the fusion perspective, Kluwer Academic Publishers, Dordrecht, 304 p., ISBN/ISNN 1- 4020-7492-1.
15. Friedman, N. ve Russell, S. (1997) Image segmentation in video sequences: A probabilistic approach, In: Proc. of the Thirteenth Annual Conf. on Uncertainty in Artificial Intelligence (UAI-97), Morgan Kaufmann Publishers Inc., San Francisco, California, USA, 175-181.
16. Fuentes, L. ve Velastin, S. (2003) From tracking to advanced surveillance, In: Proc. of IEEE Int. Conf. on Image Processing (ICIP 2003), Barcelona, Catalonia, Spain, 3, 121-124.
17. Gao, X., Boult, T., Coetzee, F. ve Ramesh, V. (2000) Error analysis of background adaption. In: Proc. of IEEE Conf. on comp. vision and pattern recognition (CVPR'00), Hilton Head Island, South Carolina, USA, 1, 503-510.
18. Halevy, G. ve Weinshall, D. (1999) Motion of disturbances: detection and tracking of multi- body non-rigid motion, Maching Vision and Applications, 11(3), 122-137, doi: 10.1007/s001380050096.
19. Heikkila, J. ve Silven, O. (1999) A real-time system for monitoring of cyclists ve pedestrians, In: Proc. of Second IEEE Workshop on Visual Surveillance, Fort Collins, Colorado, USA, 74-81.
20. Karasulu, B. (2010) Videolarda Hareketli Nesne Tespiti Ve Takibi İçin Benzetimli Tavlama Tabanlı Bir Başarım Eniyileme Yaklaşımı, (Doktora Tezi), Ege Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Anabilim Dalı.
21. Karmann, K.-P. ve Brandt, A. (1990) Moving object recognition using and adaptive background memory, 2, 289-307, Time-Varying Image Processing and Moving Object Recognition, Cappellini V. (Ed), Elsevier Science Publishers B.V., Amsterdam, Netherlands, 346p.
22. Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J., Bowers, R., Boonstra, M., Korzhova, V. ve Zhang, J. (2009) Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 319-336, doi: 10.1109/TPAMI.2008.57.
23. Lazarevic-McManus, N., Renno, J.R., Makris, D. ve Jones, G.A. (2008) An object-based comparative methodology for motion detection based on the F-Measure, Computer Vision and Image Understanding, Special Issue on Intelligent Visual Surveillance, 111(1), 74-85, doi: 10.1016/j.cviu.2007.07.007.
24. Lee, D-S., Hull, J. ve Erol, B. (2003) A Bayesian framework for gaussian mixture background modeling, In: Proc. of IEEE International Conf. on Image Processing (ICIP 2003), Barcelona, Catalonia, Spain, 3, 973-976.
25. Li, M. ve Vitànyi, P. (1997) An Introduction to Kolmogorov Complexity and Its Applications, Springer.
26. McFarlane, N. ve Schofield, C. (1995) Segmentation and tracking of piglets in images, Machine Vision and Applications, 8(3),187-193, doi: 10.1007/BF01215814.
27. Neumann, J. von (1995), Mathematische Grundlagen der Quantenmechanik (Mathematical Foundations of Quantum Mechanics). Springer, Berlin.
28. Power, P.W. ve Schoonees, J.A. (2002) Understanding background mixture models for foreground segmentation, In: Proc. of Image and Vision Computing, Auckland, New Zealand, 267-271.
29. Remagnino, P., Baumberg, A., Grove, T., Hogg, D., Tan, T.N. ve diğ. (1997) An integrated traffic and pedestrian model-based vision system, In: In Proc. of 8th British Machine Vision Conference, Essex, UK, 380-389.
30. Remagnino, P., Jones, G.A., Paragios, N. ve Regazzoni, C.S. (2002) Video-Based Surveillance Systems: Computer Vision and Distributed Processing (Eds.), Kluwer Academic Publishers, Dordrecht, 296 p., ISBN/ISSN 0-7923-7632-3.
31. Rosin, P.L. ve Ioannidis, E. (2003) Evaluation of global image thresholding for change detection, Pattern Recognition Letters, 24(14), 2345–2356.
32. Sàncheza, A.M., Patricio, M.A., Garcia, J. ve Molina, J.M. (2009) A Context Model and Reasoning System to improve object tracking in complex scenarios, Expert Systems with Applications, 36(8), 10995-11005, doi: 10.1016/ j.eswa.2009.02.096.
33. Shan, Z.Y., Yue, G.H. ve Liu, J.Z. (2002) Automated Histogram-Based Brain Segmentation in T1-Weighted Three-Dimensional Magnetic Resonance Head Images, NeuroImage, 17 (3), 1587-1598, doi: 10.1006/nimg.2002.1287.
34. Willow Garage (2012) "Willow Garage Robotic, OpenCV and Robot design", http://opencv.willowgarage.com/wiki/, (Erişim tarihi: 07.Eylül.2012).
35. Yeh, J.-Y. ve Fu, J.C. (2008) A hierarchical genetic algorithm for segmentation of multi- spectral human-brain MRI, Expert Systems with Applications, 34(2), 1285-1295, doi: 10.1016/j.eswa.2006.12.012.
36. Yilmaz, A., Javed, O. ve Shah, M. (2006) Object tracking: A survey, ACM Comput. Surveys, 38(4), Article 13, 45p, doi: 10.1145/1177352.1177355.
37. Makale 31.10.2012 tarihinde alınmış, 19.12.2012 tarihinde düzeltilmiş, 21.12.2012 tarihinde kabul edilmiştir.