You are here

VİDEOLARDAKİ HAREKETLİ NESNELERİN TESPİT VE TAKİBİ İÇİN UYARLANABİLİR ARKAPLAN ÇIKARIMI YAKLAŞIMI TABANLI BİR SİSTEM

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

Journal Name:

Publication Year:

Abstract (2. Language): 
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.
Abstract (Original Language): 
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.
FULL TEXT (PDF): 
93-110

REFERENCES

References: 

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.
Uludağ Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, Cilt 18, Sayı 1, 2013
109
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 multibody
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
Karasulu, B.: Videolardaki Hareketli Nesnelerin Tespit ve Takibi İçin UyAÇ Yaklaşımı Tabanlı Bir Sistem
110
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 multispectral
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.

Thank you for copying data from http://www.arastirmax.com