Abstract
In this paper, we address tracking of multiple people in complex 3D scenes, using multiple calibrated and synchronized far-field recordings. Our approach utilizes the faces detected in every camera view. Faces of the same person seen from the different cameras are associated by first finding all possible associations and then choosing the best option by means of a 3D stochastic tracker. The performance of the proposed system is evaluated by using the outputs of two grossly different 2D face detectors as input to our 3D algorithm. The multi-camera videos employed come from the CLEAR evaluation campaign. Even though the two 2D face detectors have very different performance, the 3D tracking performance of our system remains practically unchanged.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Z. Zhang: A Flexible New Technique for Camera Calibration, Technical Report MSR-TR-98-71, Microsoft Research, (Aug. 2002).
R. Stiefelhagen, K. Bernardin, R. Bowers, J. Garofolo, D. Mostefa and P. Soundararajan: The CLEAR 2006 Evaluation, in R. Stiefelhagen and J. Garofolo (eds.) CLEAR 2006, Lecture Notes in Computer Science, 4122 (2007), 1–44.
www.clear-evaluation.org
Stergiou, G. Karame, A. Pnevmatikakis and L. Polymenakos: The AIT 2D face detection and tracking system for CLEAR 2007, in R. Stiefelhagen, R. Bowers and J. Garofolo (eds.) CLEAR 2007, Lecture Notes in Computer Science, accepted.
G. Bradski: Computer Vision Face Tracking for Use in a Perceptual User Interface, Intel Technology Journal, 2, (1998).
M. Nechyba, L. Brandy and H. Schneiderman: PittPatt Face Detection and Tracking for the CLEAR 2007 Evaluation, in R. Stiefelhagen, R. Bowers and J. Garofolo (eds.) CLEAR 2007, Lecture Notes in Computer Science, accepted.
http://demo.pittpaftt.com
H. Schneiderman: Feature-Centric Evaluation for Efficient Cascaded Object Detection, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (June 2004).
S. Blackman: Multiple-Target Tracking with Radar Applications, Artech House, Dedham, MA (1986), chapter 14.
G. Bradski, A. Kaehler and V. Pisarevsky: Learning-Based Computer Vision with Intel’s Open Source Computer Vision Library, Intel Technology Journal, 9, (2005).
Pnevmatikakis and L. Polymenakos: Robust Estimation of Background for Fixed Cameras, in International Conference on Computing (CIC2006), (2006).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 International Federation for Information Processing
About this paper
Cite this paper
Katsarakis, N., Pnevmatikakis, A., Nechyba, M. (2007). 3D Tracking of Multiple People Using Their 2D Face Locations. In: Boukis, C., Pnevmatikakis, A., Polymenakos, L. (eds) Artificial Intelligence and Innovations 2007: from Theory to Applications. AIAI 2007. IFIP The International Federation for Information Processing, vol 247. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74161-1_40
Download citation
DOI: https://doi.org/10.1007/978-0-387-74161-1_40
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74160-4
Online ISBN: 978-0-387-74161-1
eBook Packages: Computer ScienceComputer Science (R0)