Using adaptive background subtraction into a multi-level model for traffic surveillance

Integrated Computer-Aided Engineering

Volumn 19, Issue 3, 2012, Pages 239-256

  Sanchez A ^a   Nunes, E O ^b   Conci, A ^b  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

^b FLUMINENSE FEDERAL UNIVERSITY   (Brazil)

Author keywords

Adaptive background detection; multi level image analysis; traffic linguistic description; vehicle counting and classification; video based traffic monitoring

Indexed keywords

AUTOMATIC ANALYSIS; BACKGROUND DETECTION; BACKGROUND SUBTRACTION; COMMON PROPERTY; COMPUTER VISION TECHNIQUES; ENTRY REGION; LINGUISTIC DESCRIPTIONS; LOOP DETECTOR; MULTILEVEL MODEL; REAL-TIME FRAME RATES; ROAD AREA; TRAFFIC ANALYSIS; TRAFFIC EVENT; TRAFFIC MONITORING; TRAFFIC SAFETY; TRAFFIC SURVEILLANCE; TRAFFIC VIDEOS; VARIABLE ILLUMINATION; VEHICLE COUNTING; VIDEO FRAME; VIDEO SENSORS; WEATHER CONDITIONS;

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; LINGUISTICS; PIXELS; QUANTUM CHEMISTRY; TRAFFIC CONTROL;

MOTOR TRANSPORTATION;

EID: 84863700634     PISSN: 10692509     EISSN: 18758835     Source Type: Journal    
DOI: 10.3233/ICA-2012-0402     Document Type: Conference Paper

References (57)

1. E.Atkociunas, R. Blake and A. Juozapavicius, Image Processing in Road Traffic Analysis, Nonlinear Analysis: Modelling and Control 10 (2005), 315-332.
2. Autoscope. URL: http://www.autoscope.com.
3. R.P. Avery, Y. Wang and G.S. Rutherford, Length-Based Vehicle Classification Using Images from Uncalibrated Video Cameras, Proc. 7th Intl, IEEE Conference on Intelligent Transportation Systems, 2005, pp. 737-742.
4. E. Bas, A.M. Tekalp and F.S. Salman, Automatic Vehicle Counting from Video for Traffic Flow Analysis, Proc. IEEE Intelligent Vehicles Symposium, 2007, pp. 392-397.
5. A. Bhaskar, E. Chung and A.G. Dumont, Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks, Computer-Aided Civil and Infrastructure Engineering 26(6) (2011), 433-450.
6. M. Brielmann and F.J. Rammig, Evaluating hardware design principles for the development of computer based systems, Proc. IEEE Symposium on Engineering of Computer-based Systems, 1996.
7. S. Brutzel, B.Hoferlin andG.Heidemann, Evaluation of background subtraction techniques for video surveillance, Proc. 24th IEEE Conference onComputerVision and Pattern Recognition, 2011, pp. 1937-1944.
8. N. Buch, S.A. Velastin and J. Orwell, A Review of Computer Vision Techniques for the Analysis of Urban Traffic, IEEE Trans Intelligent Transportation Systems 12(3) (2011), 920-939.
9. A. Cavallaro, O. Steiger and T. Ebrahimi, Multiple video object tracking in complex scenes, Proc. ACMMultimedia 2002, pp. 523-532.
10. X. Chen and C. Zhang, Vehicle Classification from Traffic Surveillance Videos at a Finer Granularity, Proc. MMM'07, LNCS 4351, Part I, Springer, 2007, pp. 772-781.
11. S.S.-C. Cheung and C. Kamath, Robust techniques for background subtraction in urban traffic video, EURASIP Journal on Applied Signal Processing, 2005, pp. 2330-2340. (Pubitemid 43016374)
12. J.P. Crisostomo-Romero, Vehicle counting system using realtime video processing, Proc. SPIE, vol. 6063, 2006.
13. R. Cucchiara, M. Piccardi and P. Mello, Image Analysis and Rule-Based Reasoning for a Traffic Monitoring System, IEEE Transactions on Intelligent Transportation Systems 1(2) (2000), 119-130.
14. K.G. Derpanis and R.P.Wildes, Classification of Traffic Video Based on a Spatiotemporal Orientation Analysis, Proc. Workshop on Applications of Computer Vision, 2011.
15. D. Dorantes et al., A Review of Sensing Techniques for Real-Time Surveillance, Journal of Applied Sciences 11(1) (2011), 192-198.
16. A. Elgammal, D. Hanvood and L.S. Davis, Nonparametric model for background subtraction, Proc. ECCV, 2000.
17. G.M. Faustino, M. Gattass, C.J.P. de Lucena, P.B. Campos and S.K. Rehen, A graph-mining algorithm for automatic detection and counting of embryonic stem cells in fluorescence microscopy images, Integrated Computer-Aided Engineering 18(1) (2011), 91-106.
18. A. Francois and G. Medioni, Adaptive Color Background Modeling for Real-Time Segmentation of Video Streams, Proc. of International on Imaging Science, Systems, and Technology, 1999, pp. 227-232.
19. H. Glasl, D. Schreiber, N. Viertl, S. Veigl and G. Fernandez, Video based Traffic Congestion Prediction on an Embedded System, Proc. 11th International IEEE Conf, on Intelligent Transportation Systems, 2008, pp. 950-955.
20. J.W. Hsieh, S.-H. Yu, Y.-S. Chen and W.-F. Hu, Automatic traffic surveillance systems for vehicle tracking and classification, Proc. IEEE Conf. on Intelligent Transportation Systems 7 (2006), 175-187.
21. W. Hu, T. Tan, L. Wang and S. Maybank, A survey on visual surveillance of object motion and behaviours, IEEE Trans on Systems, Man, and Cybernetics Part C: Application and Reviews 34(3) (2004), 334-352.
22. C.L. Huang and W.C. Liao, A vision-based vehicle identification system, Proc. ICPR 4 (2004), 364-367.
23. Institut f̈ur Algorithmen und Kognitive Systeme,Universiẗat Karlsruhe. URL: http://i21www.ira.uka.de/image sequences/.
24. Ipsotek. URL: http://www.ipsotek.com.
25. Y. Jin and S. Geman, Context and hierarchy in a probabilistic image model, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2006.
26. V. Kastrinaki, M. Zervakis and K. Kalaitzakis, A survey of video processing techniques for traffic applications, Image and Vision Computing 21 (2003), 359-381.
27. J. Kramer, Is abstraction the key to computing? Communications of the ACM 50(4) (2007), 36-42.
28. A.H.S. Lai and N.H.C. Yung, Vehicle-type identification through automated virtual loop assignment and block-based direction-biased motion estimation, IEEE Trans ITS 1(2) (2000), 86-97.
29. B.P.L. Lo and S.A. Velastin, Automatic congestion detection system for underground platforms, Proc. SIMP, 2001, pp. 158-161.
30. I. Marti, V.R. Tomas, L.A. Garcia and J.J. Martinez, A multiagent system for managing adverse weather situations on the road network, Integrated Computer-Aided Engineering 17(2) (2010), 145-155.
31. N. McFarlane and C. Schofield, Segmentation and tracking of piglets in images, Machine Vision and Applications 8(3) (1995), 187-193.
32. S. Messelodi et al., A computer vision system for the detection and classification of vehicles at urban road intersections, Pattern Anal Appl 8(1-2) (2005), 17-31. (Pubitemid 41649092)
33. G. Monteiro, J.Marcos, M. Ribeiro and J. Batista, Robust segmentation for outdoor traffic surveillance, Proc. 15th IEEE International Conference on Image Processing (ICIP'08), 2008, pp. 2652-2655.
34. B. Morris and M. Trivedi, Improved Vehicle Classification in Long Traffic Video by Cooperating Tracker and Classifier Modules, Proc. IEEE Intl. Conf. on Video and Signal Based Surveillance (AVSS'06), 2006.
35. A. Nagai, Y. Kuno and Y. Shirai, Surveillance system based on spatiotemporal information, Proc. ICIP, 1996, 593-596.
36. E. Nunes, A. Conci and A. Sanchez, Robust Background Subtraction on Traffic Videos, Proc. IWSSIP'11, 2011, pp. 131-134.
37. A.B. de Oliveira and J. Scharcanski, Vehicle Counting and Trajectory Detection Based on Particle Filtering, Proc. Conf. Graphics, Patterns and Images, 2010, pp. 376-383.
38. C. Ozkurt and F. Camci, Automatic Traffic Density Estimation; Vehicle Classification for Traffic Surveillance Systems using ANN, Mathematical Computational Applications 14(3) (2009), 187-196.
39. M. Piccardi, Background subtraction techniques: A review, Proc. Conf. on SMC 4 (2004), 3099-3104. (Pubitemid 40408308)
40. M. Piccardi and T. Jan, Efficient mean-shift background subtraction, Proc. ICIP, 3399-3402, 2004.
41. B. Ristic, S.Arulampalam andN.Gordon, Beyond theKalman filter: Particle filters for tracking applications, 2004.
42. T. Rodriguez and N. Garcia, An adaptive, real-time, traffic monitoring system, Machine Vision and Applications 21(4) (2010), 555-576.
43. R. Solea and U. Nunes, Trajectory planning and slidingmode control based trajectory-tracking for cybercars, Integrated Computer-Aided Engineering 14(1) (2007), 33-47.
44. C. Stauffer and W. Grimson, Adaptive background mixture models for real-time tracking, Proc. CVPR, 246.252, 1999.
45. E. Tan and J. Chen, Vehicular traffic density estimation via statistical methods with automated state learning, Proc. AVSS, 2007, 164-169.
46. X. Tao,Y. Li andR.Nayak, Knowledge RetrievalModelUsing Ontology Mining and User Profiling, Integrated Computer-Aided Engineering 15(4) (2008), 313-329.
47. B. Tian, Q. Yao, Y. Gu, K. Wang and Y. Li, Video Processing Techniques for Traffic FlowMonitoring: A Survey, Proc. 14th Intl. IEEE Conf. on Intelligent Transportation Systems, 2011, pp. 1103-1108.
48. K. Touil, M. Zribi and M. Benjelloun, Application of transferable belief model to vehicle navigation system, Integrated Computer-Aided Engineering 14(1) (2007), 93-105.
49. Y. Tsai and Y. Huang, Automatic Detection of Deficient Video Log Images Using a Histogram Equity Index and an Adaptive Gaussian Mixture Model, Computer-Aided Civil and Infrastructure Engineering 25(7) (2010), 479-493.
50. H.Wang andD. Suter, Aconsensus-basedmethod for tracking: Modelling background scenario and foreground appearance, Pattern Recognition 40 (2007), 1091-1105. (Pubitemid 44667747)
51. G. Wang, D. Xiao and J. Gu, Review on Vehicle Detection BasedonVideofor Traffic Surveillance, Proc. IEEE Intl. Conf. on Automation and Logistics, 2008, pp. 2961-2966.
52. C.R.Wren,A.Azarbayejani, T.Darrell andA. Pentland, Pfinder: Real-time tracking of the human body, IEEE Trans. on PAMI 19(7) (1997), 780-785. (Pubitemid 127762271)
53. M. Wu and H. and Adeli, Wavelet-Neural Network Model for Automatic Traffic Incident Detection, Mathematical & Computational Applications 6(2) (2001), 85-96. (Pubitemid 32724431)
54. A. Yoneyama, C.-H. Yeh and C.-C.J. Kuo, Robust Vehicle and Traffic Information Extraction for Highway Surveillance, EURASIP J Adv Sig Processing 14 (2005), 2305-2321. (Pubitemid 43016372)
55. L.A. Zadeh, From computing with numbers to computing with words, IEEE Trans. Circuits and Systems 45(1) (1999).
56. G. Zhang, R.P. Avery and Y. Wang, A Video-based Vehicle Detection and Classification System for Real-time Traffic Data Collection using Uncalibrated Cameras, Journal of the Transportation Research Board 1993 (2007), 138-147.
57. J. Zhou, D. Gao and Z. Zhang, Moving vehicle detection for automatic traffic monitoring, IEEE Transactions on Vehicular Technology 56(1) (2007), 51-59. (Pubitemid 46358010)