Hongli Deng
ABSTRACT
This paper presents a fast forensic video events analysis and retrieval system in a geospatial framework. Starting from tracking targets and analyzing video streams from distributed camera networks, the system generates video tracking metadata for each video, maps and fuses them in a uniform geospatial coordinate. The combined metadata is saved into spatial database where target trajectories are represented in geometry and geography data type. Powered by spatial functions of database, various video events such as crossing a line, entering an area, loitering and meeting, are detected by executing stored procedures that we have implemented. Geographic information system(GIS) data of Tiger-Line and GeoNames are integrated with this system to provide contextual information for more advanced forensic queries. A semantic data mining system is also attached to generate text descriptions of events and scene contextual information. The NASA World Wind is the geobrowser used to submit queries and visualize result. The main contribution of this system is that it initiates in running video event retrieval using geospatial computing techniques. This interdisciplinary combination makes this system scalable and manageable for large amount of video data from distributed cameras. It also makes the online video search possible by filtering tremendous amount of data efficiently using geospatial index techniques. From the application point of view, it extends the frontier of geospatial application by presenting a forward-looking application model.
- S. Ay, L. Zhang, S. Kim, M. He, and R. Zimmermann. Grvs: A georeferenced video search engine. In Proceedings of the seventeen ACM international conference on Multimedia, pages 977--978. ACM, ACM, 2009. Google Scholar
Digital Library
- W. G. C. Stauffer. Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):747--757, Aug. 2000. Google ScholarDigital Library
- M. Campbell, A. Haubold, S. Ebadollahi, M. Naphade, A. Natsev, J. Seidl, J. Smith, J. TeZic, and L. Xie. Ibm research trecvid-2006 video retrieval system. In NIST TRECVID-2006 Workshop, 2006.Google Scholar
- S. Chang, W. Chen, H. Meng, H. Sundaram, and D. Zhong. A fully automated content-based video search engine supporting spatiotemporal queries. IEEE Transactions on Circuits and Systems for Video Technology, 8(5):602--615, Sept. 1998. Google ScholarDigital Library
- A. Hakeem, M. W. Lee, O. Javed, and N. Haering. Semantic video search using natural language queries. In International Conference on Multimedia. ACM, 2009. Google ScholarDigital Library
- R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University, England, 2003. Google ScholarDigital Library
- W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, and S. Maybank. A system for learning statistical motion patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9):1450--1464, Sept. 2006. Google ScholarDigital Library
- W. Hu, D. Xie, Z. Fu, W. Zeng, and S. Maybank. Semantic-based surveillance video retrieval. IEEE Transactions on Image Processing, 16(4):1168--1181, Apr. 2007. Google ScholarDigital Library
- O. Javed, Z. Rasheed, K. Shafique, and M. Shah. Tracking across multiple cameras with disjoint views. In Proceedings of the IEEE international conference on computer vision, pages 1207--1216. IEEE, 2003. Google ScholarDigital Library
- N. Johnson and D. Hogg. Learning the distribution of object trajectories for event recognition. In Proceedings of the 6th British conference on Machine vision, pages 583--592. BMVA Press, 1995. Google ScholarDigital Library
- M. W. Lee, A. Hakeem, N. Haering, and S.-C. Zhu. Save: A framework for semantic annotation of visual events. In First Workshop on Internet Vision. IEEE, 2008.Google Scholar
- R. Nevatia, J. Hobbs, and B. Bolles. An ontology for video event representation. In Workshop on Event Detection and Recognition. IEEE, 2004. Google ScholarDigital Library
- Y. Sheikh and M. Shah. Trajectory association across multiple airborn cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):361--367, Feb. 2008. Google ScholarDigital Library
- L. Vincent. Taking online maps down to street level. Computer, 40(12):118--120, Dec. 2007. Google ScholarDigital Library
- X. Xiong, B. Wang, and D. Wang. Research of event-based emergency video surveillance system. In Proceedings of the 2009 International Workshop on Information Security and Application. Academy Publisher, Finland, 2009.Google Scholar
- H. Zhang, J. Wu, D. Zhong, and S. W. Smoliar. An integrated system for content-based video retrieval and browsing. Pattern Recognition, 30(4):643--658, Apr. 1997.Google ScholarCross Ref
Index Terms
- Fast forensic video event retrieval using geospatial computing
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