[en] Many computer vision systems try to infer semantic information
about a video scene content by looking at the time series
of the silhouettes of the moving objects. This paper proposes
a new inter-frame feature set (signature) based on piecewise
surfacic descriptions of binary silhouettes. It captures the dynamics
of moving objects and compacts it into a robust set of
features suitable for classification. To assess its ability to represent
motion information, we use it to build a complete gait
recognition algorithm that we test on a database of 21 different
subjects. To highlight the efficiency of our signature,
we use frontal views instead of side views of persons, which
is less discussed in literature and is considered to be harder
as the movement of legs is not visible. In that context, the
high recognition rates obtained (over 95% of correct identifications)
proves that our signature is appropriate to describe
moving objects.
Research center :
Intelsig Telim
Disciplines :
Electrical & electronics engineering
Author, co-author :
Barnich, Olivier ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Techniques du son et de l'image
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
Design of a morphological moving object signature and application to human identification
Publication date :
April 2009
Event name :
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009)
Event organizer :
IEEE
Event place :
Taipei, Taiwan
Event date :
04-2009
Audience :
International
Main work title :
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009)
Pages :
853-856
Peer reviewed :
Peer reviewed
Name of the research project :
Auralias
Funders :
DGTRE - Région wallonne. Direction générale des Technologies, de la Recherche et de l'Énergie
J. Aggarwal and Q. Cai, "Human motion analysis: A review," Computer Vision and Image Understanding, vol. 73, pp. 90-102, 1999.
O. Barnich, S. Jodogne, and M. Van Droogenbroeck, Robust analysis of silhouettes by morphological size distributions, vol. 4179 of Lecture Notes on Computer Science, pp. 734-745, Springer Verlag, 2006.
H. Murase and R. Sakai, "Moving object recognition in eigenspace representation: gait analysis and lip reading," Pattern Recogn. Lett., vol. 17, no. 2, pp. 155-162, 1996.
S. Niyogi and E. Adelson, "Analyzing and recognizing walking figures in XYT," Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 469-474, June 1994.
M. Nixon, T. Tan, and R. Chellappa, Human identification based on gait, Springer, 2006.
X. Zhou and B. Bhanu, "Feature fusion of side face and gait for video-based human identification," Pattern Recognition, vol. 41, no. 3, pp. 778-795, 2008.
C. Yam, M. Nixon, and J. Carter, "Automated person recognition by walking and running via model-based approaches," Pattern Recognition Letters, vol. 37, no. 5, pp. 1057-1072, 2004.
J. Foster, M. Nixon, and A. Prügel-Bennett, "Automatic gait recognition using area-based metrics," Pattern Recognition Letters, vol. 24, no. 14, pp. 2489-2497, 2003.
A. Kale, N. Cuntoor, B. Yegnanarayana, A. Rajagopalan, and R. Chellappa, "Gait analysis for human identification," in Proceedings of the International Conference on Audio-and Video-Based Person Authentication, Guildford, UK, 2003, pp. 706-714.
L. Wang, T. Tan, H. Ning, and W. Hu, "Silhouette analysis-based gait recognition for human identification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1505-1518, December 2003.
S. Mowbray and M. Nixon, "Automatic gait recognition via fourier descriptors of deformable objects," in Audio Visual Biometric Person Authentication, J. Kittler and M. Nixon, Eds. 2003, pp. 566-573, Springer.
M. Soriano, A. Araullo, and C. Saloma, "Curve spreads: a biometric from front-view gait video," Pattern Recognition Letters, vol. 25, no. 14, pp. 1595-1602, 2004.
Z. Zivkovic, "Improved adaptive gausian mixture model for background subtraction," in Proceedings of the 17th International Conference on Pattern Recognition, 2004, vol. 2, pp. 28-31.
P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine Learning, vol. 36, no. 1, pp. 3-42, 2006.