A novel technique for indexing video surveillance data

1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003

Volumn , Issue , 2003, Pages 98-106

  Keogh, Eamonn ^a   Celly, Bhrigu ^a   Ratanamahatana, Chotirat Ann ^a   Zordan, Victor ^a  

^a Computer Science and Engineering Department ^*  (United States)

Author keywords

Indexing; Lower bounding; Surveillance data; Uniform scaling

Indexed keywords

INDEXING (OF INFORMATION); MILITARY APPLICATIONS; MONITORING; MOTION ANALYSIS;

AUTOMATIC ANALYSIS; DYNAMIC TIME WARPING; INDEX OF SIMILARITIES; LOWER BOUNDING; ORDERS OF MAGNITUDE; SURVEILLANCE DATA; UNIFORM SCALING; VIDEO SURVEILLANCE;

SECURITY SYSTEMS;

EID: 37249001014     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/982452.982465     Document Type: Conference Paper

References (32)

1. Aach, J. and Church, G. (2001). Aligning gene expression time series with time warping algorithms. Bioinformatics. Volume 17, pp 495-508
2. Alon, J., Sclaroff, S., Kollios, G., and Pavlovic, V. (2003). Discovering Clusters in Motion Time-Series Data. In the Proc. of the IEEE CVPR 2003
3. Chan, K. & Fu, A. W. (1999). Efficient time series matching by wavelets. In proceedings of the 15th IEEE Int'l Conference on Data Engineering. Sydney, Australia. pp 126-133
4. Chu, K., Lam., S. & Wong, M. (1998) An Efficient Hash-Based Algorithm for Sequence Data Searching. The Computer Journal 41(6):402-415
5. Comaniciu, D., Ramesh, V., and Peter Meer (2003). Kernel-Based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5):564-575 (2003)
6. Corradini. A. (2001). Dynamic Time Warping for Off-line Recognition of a Small Gesture Vocabulary. In Proceedings of the 2nd IEEE International Conference on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems, pp. 82-89.
7. Faloutsos, C, Ranganathan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-senes databases. In Proc. ACMSIGMOD Conf, Minneapolis. pp. 419-429
8. Forsyth, D. A. and Ponce, J. (2001). Computer Vision: a modern approach, Prentice-Hall, 2001
9. Fung, W and Wong, M. (2003). Efficient Subsequence Matching for Sequences Databases under Time Warping. The 7th International Database Engineering and Application Symposium Hong Kong.
10. Gavnla, D. M. & Davis, L. S. (1995). Towards 3-d model-based tracking and recognition of human movement: a multi-view approach. In International Workshop on Automatic Face- and Gesture-Recognition
11. Hetland, M. (2003). A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences. To appear in an Edited Volume, Data Mining in Time Series Databases. Published by the World Scientific Publishing Company
12. Hu, N. and Dannenberg, R. B. (2002). A comparison of melodic database retrieval techniques using sung queries. ACM/IEEE Joint Conference on Digital Libraries, JCDL 2002, Portland, Oregon, USA, June 14-18, 2002: pp. 301-307
13. Johansson, G. (1973). Visual perception of biological motion and a model for its analysis. Perception and Psychophysics, 14:210-211, 1973.
14. Kahveci, T. & Singh, A. (2001). Variable length queries for time series data. In proceedings of the 17th Int'l Conference on Data Engineering. Heidelberg, Germany, pp 273-282
15. Kakadiaris, I. A. and Metaxas, D. (2000). Model-based estimation of 3D human motion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), pp. 1453-1459, 2000
16. Keogh, E. (2002). Exact indexing of dynamic time warping. In 28th International Conference on Very Large Data Bases. Hong Kong. pp 406-417
17. Keogh, E,. Chakrabarti, K,. Pazzani, M. & Mehrotra (2000). Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems. pp 263-286
18. Keogh, E,. Chakrabarti, K,. Pazzani, M. & Mehrotra (2001) Locally adaptive dimensionality reduction for indexing large time series databases. In Proc of ACM SIGMOD Conference on Management of Data. pp 151-162
19. Keogh, E. and Kasetty, S. (2002). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Canada. pp 102-111.
20. Kozlowski L T & Cutting J E (1977) Recognizing the gender of walkers from dynamic point-light displays. Perception and Psychophysics 21 575-580.
21. Marchesotti, L., Marcenaro, L., and Regazzoni, C. S. (2002) Tracking and counting multiple interacting pedestrian in indoor scenes, Third IEEE International Workshop on Performance Evaluation of Tracking and Surveillance June 1, 2002 (in conjunction with ECCV'02) Copenhagen, Denmark. (2002 IEEE)
22. Mikic, I., Trivedi, M., Hunter, E., and Cosman, P. (2002). Human Body Model Acquisition and Tracking using Voxel Data, International Journal of Computer Vision, 2002.
23. Mittal, A. and Davis, L. S. (2003). M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene. Anurag Mittal and Larry S. Davis. International Journal of Computer Vision. Vol. 51(3), Feb/March 2003
24. Moeslund, T. and Granum, E. (2001). A survey of computer vision-based human motion capture. CVIU, 81(3):231-268, 2001.
25. Montepare, J. M., & Zebrowitz-McArthur, L. (1988). Impressions of people created by age-related qualities of their gaits. Journal of Personality and Social Psychology, 55, 547-556.
26. Park, S., Chu, W. W., Yoon, J. & Hsu, C. (2000). Efficient searches for similar subsequences of different lengths in sequence databases. In proceedings of the 16th Int'l Conference on Data Engineering. San Diego, CA, pp 23-32
27. Perng, C., Wang, H., Zhang, S., & Parker, S. (2000). Landmarks: a new model for similarity-based pattern querying in time series databases. In proceedings of 16th International Conference on Data Engineering. pp 33-42
28. Rath, T. & Manmatha, R. (2002): Lower-Bounding of Dynamic Time Warping Distances for Multivariate Time Series. Tech Report MM-40, University of Massachusetts Amherst.
29. Roddick, J. F. and Spiliopoulou, M. (2001). A Survey of Temporal Knowledge Discovery Paradigms and Methods. IEEE Tran's on Knowledge and Data Engineering. pp. 750-767
30. Seidl, T. & Kriegel, H. (1998). Optimal multi-step k-nearest neighbor search. SIGMOD Conference. pp 154-165
31. Vlachos, M., Kollios, G., & Gunopulos, G. (2002). Discovering similar multidimensional trajectories. In Proc 18th International Conference on Data Engineering
32. Zhu, Y. & Shasha, D. (2003). Query by Humming: a Time Series Database Approach. SIGMOD 2003.