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subject Re-Acquisition (SCRA) metric at the AVSS conference, there are some
issues.
Especially those concerning computer vision techniques - object detection,
tracking and recognition performance in low quality video. First, the quality
of the embedded OpenCV methods should be extended by (many) parameters
tuning. Second, the problem is to find more reliable visual features necessary
for the object re-identification, because almost everybody wears black at the
airport and objects are represented by a few pixels. We suppose moving to the
high-definition video will results in more precise event recognition, occlusion
handling and feature extraction for the automated wide area surveillance.
References
1. BBC 'Talking' CCTV scolds offenders. BBC News (April 4, 2007)
2. Brakatsoulas, S., Pfoser, D., Tryfona, N.: Modeling, Storing and Mining Moving
Object Databases. In: IDEAS (2004)
3. Carmona, E.J., Martinez-Cantos, J., Mira, J.: A new video segmentation method
of moving objects based on blob-level knowledge. Pattern Recognition Letters 29,
272-285 (2008)
4. CARETAKER Consortium. Caretaker Puts Knowledge to Good Use. Mobility,
The European Public Transport Magazine 18(13) (2008)
5. Chmelar, P., Zendulka, J.: Visual Surveillance Metadata Management. Database
and Expert Systems Applications. In: Wagner, R., Revell, N., Pernul, G. (eds.)
DEXA 2007. LNCS, vol. 4653, pp. 79-84. Springer, Heidelberg (2007)
6. Davenport, J.: Tens of thousands of CCTV cameras, yet 80% of crime unsolved.
Evening Standard (September19, 2007)
7. Ellis, T., Black, J., Xu, M., Makris, D.: A Distributed Multi Camera Surveillance
System. Ambient Intelligence, 107-138 (2005)
8. Bradski, G.R.: Learning OpenCV, p. 555. O'Reilly, Sebastopol (2008)
9. ISO/IEC JTC1/SC29/WG11. MPEG-7 Overview (2004)
10. Javed, O., Shah, M.: Automated Visual Surveillance: Theory and Practice, p. 110.
Springer, Heidelberg (2008)
11. Mlich, J., Chmelar, P.: Trajectory classification based on Hidden Markov Models.
In: Proceedings of 18th Int. Conf. on Computer Graphics and Vision, pp. 101-105
(2008)
12. Qu, W., Schonfeld, D., Mohamed, M.: Distributed Bayesian multiple-target track-
ing in crowded environments using multiple collaborative cameras. EURASIP J.
Appl. Signal Process (1) (2007)
13. Qureshi, F.Z., Terzopoulos, D.: Multi-camera Control through Constraint Satis-
faction for Persistent Surveillance. In: IEEE Conf. on Advanced Video and Signal
Based Surveillance, pp. 211-218 (2008)
14. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision,
3rd edn., p. 800. Thomson Engineering, Toronto (2007)
15. Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: a review.
Vision, Image and Signal Processing, IEE Proceedings 152(2), 192-204 (2005)
16. HOSDB. Home Oce Multiple Camera Tracking Scenario data,
scienceandresearch.homeoffice.gov.uk/hosdb/cctv-imaging-technology/
video-based-detection-systems/i-lids [cit. 2009-11-17]
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