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Proceedings Paper

FaceTrack: tracking and summarizing faces from compressed video
Author(s): Hualu Wang; Harold S. Stone; Shih-Fu Chang
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Paper Abstract

In this paper, we present FaceTrack, a system that detects, tracks, and groups faces from compressed video data. We introduce the face tracking framework based on the Kalman filter and multiple hypothesis techniques. We compare and discuss the effects of various motion models on tracking performance. Specifically, we investigate constant-velocity, constant-acceleration, correlated-acceleration, and variable-dimension-filter models. We find that constant- velocity and correlated-acceleration models work more effectively for commercial videos sampled at high frame rates. We also develop novel approaches based on multiple hypothesis techniques to resolving ambiguity issues. Simulation results show the effectiveness of the proposed algorithms on tracking faces in real applications.

Paper Details

Date Published: 24 August 1999
PDF: 13 pages
Proc. SPIE 3846, Multimedia Storage and Archiving Systems IV, (24 August 1999); doi: 10.1117/12.360426
Show Author Affiliations
Hualu Wang, Columbia Univ. (United States)
Harold S. Stone, NEC Research Institute (United States)
Shih-Fu Chang, Columbia Univ. (United States)


Published in SPIE Proceedings Vol. 3846:
Multimedia Storage and Archiving Systems IV
Sethuraman Panchanathan; Shih-Fu Chang; C.-C. Jay Kuo, Editor(s)

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