BMVC 2004, Kingston, 7th-9th Sept, 2004
Dynamic Classifier for Non-rigid Human motion analysis
H. Fei and I. Reid (University of Oxford)
Automatic analysis (parsing) of non-rigid human motion in a cluttered outdoor
enviroment is a useful but challenging task. In a single view point,
the lack of depth order relations causes a major ambiguity of the object
identities. Coupled with the non-rigidity of articulation, 3D human motion
tracking/pose estimation in one view is a formidable problem. In this paper,
we present a novel solution that directly address this depth ambiguity, in
which we extend a discriminative analysis (Support Vector Machine (SVM))
to non-rigid human motion classification with a temporal generative motion
model (Hidden Markov Model (HMM)). This method can discriminate dynamic
depth ordering as well as 3D articulated motion automatically from 2D
images. Experiments with this method have demonstrated promising results.
(pdf article)