Anatomy of a multicamera video surveillance system

Multimedia Systems

Volumn 10, Issue 2, 2004, Pages 144-163

  Jiao, Long ^a   Wu, Yi ^a   Wu, Gang ^a   Chang, Edward Y ^a   Wang, Yuan Fang ^a  

^a University of California   (United States)

Author keywords

Image; Motion; Recognition; Surveillance; Video

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DETECTORS; DYNAMIC PROGRAMMING; IMAGE ANALYSIS; KALMAN FILTERING; LEARNING SYSTEMS; MOTION ESTIMATION; PATTERN RECOGNITION; SENSOR DATA FUSION; VIDEO CAMERAS;

DECISION TREES; MOTION TRAJECTORIES; RECOGNITION; VIDEO SURVEILLANCE SYSTEMS;

COMPUTER VISION;

EID: 10644261365     PISSN: 09424962     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00530-004-0147-2     Document Type: Article

References (53)

1. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12(6):783-789
2. Azuma R (1995) Predicative tracking for augmented reality. University of North Carolina-Chapel Hill, Department of Computer Science, TR-95-007
3. Bengio Y (1998) Markovian models for sequential data. Neural Comput Surv 2:129-162
4. Boyd JE, Hunter E, Kelly PH, Tai LC, Phillips CB, Jain RC (1998) Mpi-video infrastructure for dynamic environments. In: IEEE international conference on multimedia systems '98, June 1998
5. Bozkaya T, Ozsoyoglu M (1997) Distanced-based indexing for high-dimensional metric spaces. In: Proc. ACM SIGMOD, pp 357-368
6. Brown RG (1983) Introduction to random signal analysis and Kalman filtering. Wiley, New York
7. Burden RL, Faires JD (eds) (1993) Numerical analysis, 5th edn. PWS, New York
8. Burges CJC (1999) Geometry and invariance in kernel based methods. In: Schlkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods: support vector learning. MIT Press, Cambridge, MA
9. Christin L, Eskin E, Cohen A, Westo J, Noble WS (2002) Mismatch string kernels for svm protein classification. Neural Inf Process Syst 15:1441-1448
10. Chudova D, Smyth P (2002) Pattern discovery in sequences under a markov assumption, ACM SIGKDD
11. Church E (1945) Revised geometry of the aerial photograph. Bull Aerial Photogrammetry 15
12. Cohn H (1980) Conformal mapping on Riemann surfaces. Dover, Mineola, NY
13. Collins RT, Lipton AJ (2000) A system for video surveillance and monitoring (vsam project final report). CMU Technical Report CMU-RI-TR-00-12
14. DeMenthon DF, Davis LS (1995) Model-based object pose in 25 lines of code. Int J Comput Vision 15:123-141
15. Duda RO, Hart RE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York
16. Farin G (1997) Curves and surfaces for computer aided geometric design, 4th edn. Academic, San Diego
17. Faugeras O (1993) Three-dimensional computer vision. MIT Press, Cambridge, MA
18. Foley JD, van Dam A, Feiner SK, Hughes JF (1990) Computer graphics: principles and practice, 2nd edn. Addison-Wesley, Reading, MA
19. Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic, Boston
20. Haralick R, Joo H, Lee C, Zhuang X, Vaidya V, Kim M (1989) Pose estimation from corresponding point data. IEEE Trans Syst Man Cybern 19:1426-46
21. Haykin S (1999) Neural networks, 2nd edn. Prentice Hall, Englewood Cliffs, NJ
22. Horaud R, Dornaika F, Lamiroy B, Christy S (1997) Object pose: the link between weak perspective, paraperspective and full perspective. Int J Comput Vision 22:173-189
23. Isard M, Blake A (1998) Condensation - conditional density propagation for visual tracking. Int J Comput Vision 29:5-28
24. Isard M, Blake A (1998) ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. Lecture notes in computer science vol 1406. Springer, Berlin Heidelberg New York, pp 893-908. Int J Comput Vis 29(1):5-28
25. Isard M, Blake A (1998) ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. Lecture notes in computer science vol 1406. Springer, Berlin Heidelberg New York, pp 893-908. Int J Comput Vis 29(1):5-28
26. Jaakkola TS, Diekhans M, Haussler D (1999) Using the fisher kernel method to detect remote protein homologies. In: Proc. 7th international conference on intelligent system for molecular biology
27. Jaakkola TS, Haussler D (1998) Exploiting generative models in discriminative classifiers. In: Proceedings of the conference on advances in neural information processing systems II, pp 487-493
28. Jaakola TS, Haussler D (1999) Probabilistic kernel regression models. In: Conference on AI and statistics
29. Julier SJ, Uhlmann JK, Durrant-Whyte HF (1995) A new approach for filtering nonlinear systems. In: Proc. American Control Conference, Seattle
30. Kanatani K (1998) Optimal homography computation with a reliability measure. In: Proc. IAPR workshop on machine vision applications, November 1998
31. Kettnaker V, Zabih R (1999) Bayesian multi-camera surveillance. In: CVPR
32. Kitagawa G (1996) Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J Comput Graph Stat 5:1-25
33. Lee L, Romano R, Stein G (2000) Monitoring activities from multiple video streams: establishing a common coordinate system. IEEE Trans PAMI 22(8):758-767
34. Leslie C, Eskin E, Noble WS (2002) The spectrum kernel: a string kernel for svm protein classification. In: Proc. Pacific symposium on biocomputing. World Scientific, Singapore
35. Lou J, Yang H, Hu W, Tan T (2002) Visual vehicle tracking using an improved ekf. In: ACCV
36. Maybank SJ, Worrall AD, Sullivan GD (1996) Filter for car tracking based on acceleration and steering angle. In: Proc. British Machine Vision Conference
37. Maybeck PS (1979) Stochastic models, estimation, and control, vol 1. Academic, New York
38. Pavlidis I, Morellas V (2001) Two examples of indoor and out-door surveillance systems: motivation, design, and testing. In: Proc. 2nd European workshop on advanced video-based surveillance
39. Pavlidis I, Morellas V, Tsiamyrtzis P, Harp S (2001) Urban surveillance systems: from the laboratory to the commercial world. Proc IEEE 89(10):1478-1497
40. Pitt MK, Shephard N (1999) Filtering via simulation: Auxiliary particle filters. J Am Stat Assoc 94:590-599
41. Regazzoni C, Varshney PK (2002) Multisensor surveillance systems based on image and video data. In: Proc. IEEE conference on image processing
42. Sears FW (1958) Optics, 3rd edn. Addison-Wesley, Reading, MA
43. Starner T, Pentland A (1994) Visual recognition of American sign language using hidden Markov models. Technical Report Master's thesis, MIT, February 1995, Program in Media Arts & Sciences, MIT Media Laboratory.
44. Also Media Lab VISMOD TR 316. http://www-white.media.mit.edu/vismod/ people/publications/publications.html [860 Kb]
45. Struik DJ (1961) Differential geometry, 2nd edn. Addison-Wesley, Reading, MA
46. Vapnik V (1995) The Nature of statistical learning theory. Springer, Berlin Heidelberg New York
47. Watkins C (1999) Dynamic alignment kernels. Technical Report, Department of Computer Science, University of London
48. Welch G, Bishop G (2002) http://www.cs.unc.edu/welch/kalman
49. Welch G, Bishop G (2002) An introduction to the Kalman filter. University of North Carolina-Chapel Hill, TR 95-041
50. Wu G, Chang E Adaptive feature-space conformal transformation for learning unbalanced data. In: International conference on machine learning (ICML), August 2003
51. Xu G, Zhang Z (1996) Epipolar geometry in stereo, motion and object recognition. Kluwer, Dordrecht
52. Wu Y, Jiao L, Wu G, Wang YF, Chang E (2003) Invaraint feature extraction and biased statistical inference for video surveillance. In: Proc. IEEE conference on advanced video and signal-based surveillance, Miami, FL
53. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22:1330-4