Abstract
Omnipresent camera networks have been a popular research topic in recent years. Example applications include surveillance and monitoring of inaccessible areas such as train tunnels and bridges. Though a large body of existing work focuses on image and video processing techniques, very few address the usability of such systems or the implications of real-time video dissemination. In this paper, we present our work on extending the LVDBMS prototype with a multifaceted object model to better characterize objects in live video streams. This forms the basis for a cross camera tracking framework based on the informatics-based approach which permits objects to be tracked from one video stream to another. Queries may be defined that monitor the streams in real time for complex events. Such a new database management environment provides a general-purpose platform for distributed live video computing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38(4) (2006)
Du, W., Piater, J.: Multi-camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration. In: Asian Conf. on Computer Vision (2007)
Song, B., Roy-Chowdhury, A.: Stochastic Adaptive Tracking in a Camera Network. In: IEEE Intl. Conf. on Computer Vision (2007)
Tieu, K., Dalley, G., Grimson, W.: Inference of Non-Overlapping Camera Network Topology by Measuring Statistical Dependence. In: IEEE Intl. Conf. on Computer Vision (2005)
Velipasalar, S., Brown, L.M., Hampapur, A.: Detection of user-defined, semantically high-level, composite events, and retrieval of event queries. Multimedia Tools Appl. 50(1), 249–278 (2010)
The London Evening Standard. Tens of thousands of CCTV cameras, yet 80% of crime unsolved (2007), http://www.thisislondon.co.uk/news/article-23412867-tens-of-thousands-of-cctv-cameras-yet-80-of-crime-unsolved.do
Peng, R., Aved, A.J., Hua, K.A.: Real-Time Query Processing on Live Videos in Networks of Distributed Cameras. International Journal of Interdisciplinary Telecommunications and Networking 2(1), 27–48 (2010)
Hu, M.K.: Visual Pattern Recognition by Moment Invariants, IRE Trans. Info. Theory, vol. IT-8, pp.179-187 (1962)
Javed, O., Rasheed, Z., Shah, M.: Tracking Across Multiple Cameras with Disjoint Views. In: The Ninth IEEE International Conference on Computer Vision (ICCV), Nice, France (2003)
Javed, O., Rasheed, Z., Shah, M.: Modeling Inter-Camera Space-Time and Appearance Relationships for Tracking across Non-Overlapping Views. Computer Vision and Image Understanding Journal 109(2) (February 2008)
Hampapur, A., Brown, L., Connell, J., Ekin, A., Haas, N., Lu, M., et al.: Smart Video Surveillance, Exploring the concept of multi-scale spatiotemporal tracking. IEEE Signal Processing Magazine (March 2005)
Cheng, H., Hua, K.A., Yu, N.: An Automatic Feature Generation Approach to Multiple Instance Learning and its Applications to Image Databases. In: The International (2009)
Chen, X., Zhang, C., Chen, S., Chen, M.: A latent semantic indexing based method for solving multiple instance learning problem in region-based image retrieval. In: Seventh IEEE International Symposium on Multimedia, pp. 8, 12–14 (2005)
CAVIAR: Context Aware Vision using Image-based Active Recognition (2011) http://homepages.inf.ed.ac.uk/rbf/CAVIAR/
Adali, S., Candan, K.S., Chen, S., Erol, K., Subrahmanian, V.S.: Advanced video information systems: data structures and query processing. ACM Multimedia Systems 4, 172–186 (1996)
Donderler, M.E., Saykol, E., Ulusoy, O., Gudukbay, U.: BilVideo: A video database management system. IEEE Multimedia 1(10), 66–70 (2003)
Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., et al.: Query by image and video content: The QBIC system. IEEE Computer 28, 23–32 (1995)
Jiang, H., Montesi, D., Elmagarmid, A.K.: VideoText database systems. In: Proceedings of IEEE Multimedia Computing and Systems, pp. 344–351 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aved, A.J., Hua, K.A., Gurappa, V. (2011). An Informatics-Based Approach to Object Tracking for Distributed Live Video Computing. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2011. Communications in Computer and Information Science, vol 149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21512-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-21512-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21511-7
Online ISBN: 978-3-642-21512-4
eBook Packages: Computer ScienceComputer Science (R0)