Video Visualization

ABSTRACT

Video data, generated by the entertainment industry, security and traffic cameras, video conferencing systems, video emails, and so on, is perhaps most time-consuming to process by human beings. In this paper, we present a novel methodology for "summarizing" video sequences using volume visualization techniques. We outline a system pipeline for capturing videos, extracting features, volume rendering video and feature data, and creating video visualization. We discuss a collection of image comparison metrics, including the linear dependence detector, for constructing "relative" and "absolute" difference volumes that represent the magnitude of variation between video frames. We describe the use of a few volume visualization techniques, including volume scene graphs and spatial transfer functions, for creating video visualization. In particular, we present a stream-based technique for processing and directly rendering video data in real time. With the aid of several examples, we demonstrate the effectiveness of using video visualization to convey meaningful information contained in video sequences.

References

1. BROCKE, M. 2002. Statistical image sequence processing for temporal change detection. In Proc. 24th DAGM Symposium: Pattern Recognition, Springer, LNCS 2449, Zurich, Switzerlan, 215-223.
2. CAVALLARO, A., AND EBRAHIMI, T. 2001. Change detection based on color edges. In Proc. IEEE International Symposium on Circuits and Systems (ISCAS-2001), Sydney, Australia, 141-144.
3. CHEN, M., AND TUCKER, J. 2000. Constructive volume geometry. Computer Graphics Forum 19, 4, 281-293.
4. CHEN, M., SILVER, D., WINTER, A. S., SINGH, V., AND CORNEA, N. 2003. Spatial transfer functions - a unified approach to specifying deformation in volume modeling and animation. In Proc. Volume Graphics 2003, Tokyo, Japan, Eurographics/ACM Publications, 35-44.
5. COLLINS, R., LIPTON, A., KANADE, T., FUJIYOSHI, H., DUGGINS, D., TSIN, Y., TOLLIVER, D., ENOMOTO, N., AND HASEGAWA, O. 2000. A system for video surveillance and monitoring. Tech. Rep. CMU-RITR-00-12, Carnegie Mellon University, Robotics Institute, May.
6. CUTLER, R., SHEKHAR, C., BURNS, B., CHELLAPPA, R., BOLLES, R., AND DAVIS, L. 1999. Monitoring human and vehicle activities using airborne video. In Proc. 28th Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC.
7. DURUCAN, E., AND EBRAHIMI, T. 2001. Change detection and background extraction by linear algebra. Proceedings of the IEEE 89, 10, 1368-1381.
8. DURUCAN, E., AND EBRAHIMI, T. 2001. Improved linear dependence and vector model for illumination invariant change detection. In Proc. SPIE, vol. 4303, San Jose, California.
9. HAJEK, J. 2002. Time reconstruction of video sequence. In Electronic Proc. Central European Seminar on Computer Graphics, http://www.cg.tuwien.ac.at/studentwork/CESCG/CESCG-2002/.
10. HERTZMANN, A., AND PERLIN, K. 2000. Painterly rendering for video and interaction. In Proc. 1st International Symposium on Non-Photorealistic Animation and Rendering, Annecy, France, 7-12.
11. KLEIN, A. W., SLOAN, P. J., FINKELSTEIN, A., AND COHEN, M. F. 2002. Stylized video cubes. In Proc. ACM SIGGRAPH Symposium on Computer Animation, San Antonio, Texas, 15-22.
12. LEVOY, M. 1988. Display of surfaces from volume data. IEEE Computer Graphics and Applications 8, 3, 29-37.
13. LORENSEN, W., AND CLINE, H. 1987. Marching cubes: a high resolution 3D surface construction algorithm. In Proc. SIGGRAPH'87, vol. 21(4), Anaheim, California, 163-169.
14. NARASIMHAN, S.,WANG, C., AND NAYAR, S. 2002. All the images of an outdoor scene. In Computer Vision - ECCV (3), Springer, LNCS 2352, Copenhagen, Denmark, 148-162.
15. ROSIN, P. 1997. Thresholding for change detection. In Proc. 8th British Machine Vision Conference, Essex, UK, 212-221.
16. STRINGA, E. 2000. Morphological change detection algorithms for surveillance applications. In Proc. 11th British Machine Vision Conference, Bristol, UK, 402-411.
17. TOTH, D., AACH, T., AND METZLER, V. 2000. Illumination-invariant change detection. In Proc. 4th IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, Texas, 3-7.
18. TSEKERIDOU, S., KRINIDIS, S., AND PITAS, I. 2001. Scene change detection based on audio-visual analysis and interaction. In Multi-Image Analysis, Springer, LNCS 2032, Dagstuhl, Germany, 214-225.
19. VANNOORENBERGHE, P., MOTAMED, C., BLOSSEVILLE, J.-M., AND POSTAIRE, J.-G. 1997. Automatic pedestrian recognition using real-time motion analysis. In Proc. ICIAP 97 Image Analysis and Processing, LNCS 1311, Florence, Italy, 493-500.
20. WAKEFIELD, J. 2002. Watching your every move. BBC News Online (7 February). http://news.bbc.co.uk.
21. WINTER, A., AND CHEN, M. 2002. Image-swept volumes. Computer Graphics Forum 21, 3, 441-450.
22. YEO, B.-L., AND YEUNG, M. 1997. Retrieving and visualizing video. Communications of the ACM 40, 12, 43-52.
23. YIN, J. H., ZHANG, X., VELASTIN, S. A., AND DAVIES, A. C. 1996. Incident detection in pedestrian traffic using image processing. In Proc. 8th IEE International Conference on Road Traffic Monitoring and Control, London, UK, 115-119.
24. YOUNG, S., FORSHAW, M., AND HODGETTS, M. 1999. Image comparison methods for perimeter surveillance. In Proc. 7th IEE International Conference on Image Processing and its Applications, 465, 799-803.
25. ZHOU, H., CHEN, M., AND WEBSTER, M. F. 2002. Comparative evaluation of visualization and experimental results using image comparison metrics. In Proc. IEEE Visualization 2002, Boston, MA, 315-322.