TY - GEN
T1 - A non-parametric hierarchical model to discover behavior dynamics from tracks
AU - Kooij, Julian F.P.
AU - Englebienne, Gwenn
AU - Gavrila, Dariu M.
PY - 2012
Y1 - 2012
N2 - We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by various behaviors and represent spatially localized occurrences of a person's low-level motion dynamics using Switching Linear Dynamics Systems. Since the model handles real-valued features directly, we do not lose information by quantizing measurements to 'visual words' and can thus discover variations in standing, walking and running without discrete thresholds. We describe inference using Gibbs sampling and validate our approach on several artificial and real-world tracking datasets. We show that our model can distinguish relevant behavior patterns that an existing state-of-the-art method for hierarchical clustering cannot.
AB - We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by various behaviors and represent spatially localized occurrences of a person's low-level motion dynamics using Switching Linear Dynamics Systems. Since the model handles real-valued features directly, we do not lose information by quantizing measurements to 'visual words' and can thus discover variations in standing, walking and running without discrete thresholds. We describe inference using Gibbs sampling and validate our approach on several artificial and real-world tracking datasets. We show that our model can distinguish relevant behavior patterns that an existing state-of-the-art method for hierarchical clustering cannot.
UR - http://www.scopus.com/inward/record.url?scp=84867867522&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33783-3_20
DO - 10.1007/978-3-642-33783-3_20
M3 - Conference contribution
AN - SCOPUS:84867867522
SN - 9783642337826
VL - 7577 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 270
EP - 283
BT - Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
T2 - 12th European Conference on Computer Vision, ECCV 2012
Y2 - 7 October 2012 through 13 October 2012
ER -