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Clear face analysis from MPEG compressed video
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Source International Multimedia Conference archive
Proceedings of the tenth ACM international conference on Multimedia table of contents
Juan-les-Pins, France
DEMONSTRATION SESSION: Demonstration session 1 table of contents
Pages: 95 - 96  
Year of Publication: 2002
ISBN:1-58113-620-X
Authors
Ling-Yu Duan  Laboratories for Information Technology, Agency for Science, Technology and Research, Singapore
Qi Tian  Laboratories for Information Technology, Agency for Science, Technology and Research, Singapore
Sponsors
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this demonstration, we present a system to analyze the clear degree of faces present in MPEG compressed video of Head-and-Shoulders style. The proposed system consists of three hierarchical modules: low-level features extraction, robust face tracking, and clear faces selection. We have integrated the core algorithm into an Automated Transaction Service (ATS) surveillance system. The Incremental Focus of Attention (IFA) architecture is taken to combine pixel domain processing with compressed domain processing --- thus, implemented system exhibits computational efficiency and tolerance to very cluttered scenes. The proposed system has successively detected segments with clear frontal faces from more than 20 Automated Teller Machine (ATM) testing clips in MPEG format, each of which consists of 1~3 transactions. In addition, the proposed scheme implies some potential video mining applications, such as automatic checking to verify entry authorization, retrieval of suspicious activities in prerecorded video surveillance sequences.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Bradski G. Computer Vision Face Tracking for Use in a Perceptual User Interface. Intel Technology Journal, Q2, 1998.
 
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P. De Smet, R.L. Pires, D. De Wleeschauwer. Activity Driven Non-linear Diffusion for Color Image Watershed Segmentation. SPIE Journal of Electronic Imaging, volume 8, no 3, pages 270--278, 1999.