Structural similarity-based object tracking in multimodality surveillance videos

Machine Vision and Applications

Volumn 20, Issue 2, 2009, Pages 71-83

  Łoza, Artur ^a   Mihaylova, Lyudmila ^b   Bull, David ^a   Canagarajah, Nishan ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Colour and edge cues; Multimodal data; Object tracking; Particle filtering; Structural similarity measure; Video sequences

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; COLOR; EDGE DETECTION; ENCODING (SYMBOLS); NONLINEAR FILTERING; SENSORS; SIGNAL FILTERING AND PREDICTION; VIDEO RECORDING;

COLOUR AND EDGE CUES; MULTIMODAL DATA; OBJECT TRACKING; PARTICLE FILTERING; STRUCTURAL SIMILARITY MEASURE; VIDEO SEQUENCES;

SECURITY SYSTEMS;

EID: 58549097662     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-007-0107-x     Document Type: Article

References (39)

1. F. Aherne N. Thacker P. Rockett 1997 The Bhattacharyya metric as an absolute similarity measure for frequentcy coded data Kybernetica 32 4 1 7
2. M. Arulampalam S. Maskell N. Gordon T. Clapp 2002 A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Trans. Signal Proc. 50 2 174 188
3. P. Brasnett L. Mihaylova D. Bull N. Canagarajah 2007 Sequential Monte Carlo tracking by fusing multiple cues in video sequences Image Vis. Comput. 25 8 1217 1227
4. Brasnett, P., Mihaylova, L., Canagarajah, N., Bull, D.: Particle filtering with multiple cues for object tracking in video sequences. In: Proceedings of SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology, Vol. 5685, pp. 430-441 (2005)
5. D. Comaniciu P. Meer 2002 Mean shift: a robust approach toward feature space analysis IEEE Trans. Pattern Anal. Mach. Intell. 22 5 603 619
6. D. Comaniciu V. Ramesh P. Meer 2003 Kernel-based object tracking IEEE Trans. Pattern Anal. Mach. Intell. 25 5 564 577
7. Cvejic, N., Nikolov, S.G., Knowles, H.D., Loza, A., Achim, A., Bull, D.R., Canagarajah, C.N.: The effect of pixel-level fusion on object tracking in multi-sensor surveillance video. In: Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pp. 1-7 (2007)
8. Doucet A., Freitas N., Gordon N. (2001). Sequential Monte Carlo Methods in Practice. Springer, New York
9. G. Foresti C. Micheloni L. Snidaro P. Remagnino T. Ellis 2005 Active video-based surveillance system: the low-level image and video processing techniques needed for implementation IEEE Signal Process. Mag. 22 2 25 37
10. Forsyth, D., Arikan, O., Ikemoto, L., Ramanan, D.: Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis. Foundations and Trends in Computer Graphics and Vision. Now Publishers Inc, Hanover (2006)
11. A. Hampapur L. Brown J. Connell A. Ekin N. Haas M. Lu H. Merkl S. Pankanti 2005 Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking IEEE Signal Process. Mag. 22 2 38 51
12. W. Hu T. Tan L. Wang S. Maybank 2004 A survey on visual surveillance of object motion and behaviors IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 34 3 334 352
13. Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: European Conf. on Computer Vision, pp. 343-356. Cambridge (1996)
14. M. Isard A. Blake 1998 Condensation-conditional density propagation for visual tracking Intl. J. Comput. Vis. 28 1 5 28
15. Lewis, J.J., Nikolov, S.G., Loza, A., Canga, E.F., Cvejic, N., Li, J., Cardinali, A., Canagarajah, C.N., Bull, D.R., Riley, T., Hickman, D., Smith, M.I.: The Eden project multi-sensor data set. In: Technical Report TR-UoB-WS-Eden-Project-Data-Set. Available at http://www.imagefusion.org (2006)
16. J.J. Lewis R.J. O'Callaghan S.G. Nikolov D.R. Bull N. Canagarajah 2007 Pixel- and region-based image fusion with complex wavelets Inf. Fusion 8 2 119 130
17. J. Liu R. Chen 1998 Sequential Monte Carlo methods for dynamic systems J. Am. Stat. Assoc. 93 443 1032 1044
18. Loza, A., Dixon, T.D., Canga, E.F., Nikolov, S.G., Bull, D.R., Canagarajah, C.N., Noyes, J.M., Troscianko, T.: Methods of fused image analysis and assessment. In: Proceedings of the Advanced Study Institute Conference, Albena, Bulgaria, 16-27 May, pp. 252-259 (2005)
19. Loza, A., Mihaylova, L., Bull, D.R., Canagarajah, C.N.: Structural similarity-based object tracking in video sequences. In: Proceedings of the 9th International Conf. on Information Fusion, Florence, Italy, 10-13 July (2006)
20. S. McKenna S. Jabri Z. Duric A. Rosenfeld H. Wechsler 2000 Tracking groups of people Comput. Vis. Image Underst. 80 1 42 56
21. Mihaylova, L., Loza, A., Nikolov, S.G., Lewis, J., Canga, E.F., Li, J., Bull, D.R., Canagarajah, C.N.: The influence of multi-sensor video fusion on object tracking using a particle filter. In: Proceedings of the 2nd Workshop on Multiple Sensor Data Fusion, Dresden, Germany, 2-6 October, pp. 354-358 (2006)
22. Moreno-Noguer, F., Sanfeliu, A., Samaras, D.: Integration of dependent Bayesian filters for robust tracking. In: Proceedings of the 2006 International Conference on Robotics and Automation, pp. 4081-4067 (2006)
23. PETS 2006 benchmark data. Dataset available on-line at: http://www.pets2006.net (2006)
24. K. Nummiaro E. Koller-Meier L.V. Gool 2003 An adaptive color-based particle filter Image Vis. Comput. 21 1 99 110
25. Nummiaro, K., Koller-Meier, E.B., Gool, L.V.: A color-based particle filter. In: Proceedings of 1st International Workshop on Generative-Model-Based Vision GMBV'02, in conjuction with ECCV'02, pp. 53-60 (2002)
26. PerceptiVU, Inc.: Target Tracking Movie Demos. http://www.perceptivu.com
27. P. Pérez J. Vermaak A. Blake 2004 Data fusion for tracking with particles Proc. IEEE 92 3 495 513
28. Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on, vol. 3, pp. III-173-6 vol. 2 (2003)
29. Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, London (2004)
30. Shen, C., van den Hengel, A., Dick, A.: Probabilistic multiple cue integration for particle filter based tracking. In: Proceedings of the VIIth Digital Image Computing : Techniques and Applications. Sun, C., Talbot, H., Ourselin, S., Adriansen, T. (eds.) (2003)
31. Tao, D., Li, X., Wu, X., Maybank, S.: Human carrying status in visual surveillance. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 670-677 (2006)
32. D. Tao X. Li X. Wu S.J. Maybank 2007 General tensor discriminant analysis and Gabor features for gait recognition IEEE Trans. Pattern Anal. Mach. Intell. 29 10 1700 1715
33. K. Toyama G. Hager 1999 Incremental focus of attention for robust vision-based tracking Int. J. Comput. Vision 35 1 45 63
34. Triesch, J.: Self-organized integration of adaptive visual cues for face tracking. In: Sensor Fusion: Architectures, Algorithms, and Applications IV. Dasarathy, B.V. (ed.) Proceedings of SPIE, vol. 4051, pp. 397-406 (2000)
35. Wan, E., van der Merwe, R.: The Unscented Kalman filter. In: Haykin, S. (ed.) Kalman Filtering and Neural Networks, chap. 7, pp. 221-280. Wiley, London (2001)
36. X. Wang B. Xiao J.F. Ma X.L. Bi 2007 Scaling and rotation invariant analysis approach to object recognition based on radon and fourier-mellin transforms Pattern Recognit. 40 12 3503 3508
37. Z. Wang A. Bovik H. Sheikh E. Simoncelli 2004 Image quality assessment: from error visibility to structural similarity IEEE Trans. Image Process. 13 4 600 612
38. Wang, Z., Bovik, A.C., Simoncelli, E.P.: Structural approaches to image quality assessment. In: Bovik, A. (ed.) Handbook of Image and Video Processing, 2nd edn, chap. 8.3. Academic, New York (2005)
39. Webb A. (2003). Statistical Pattern Recognition. Wiley, London