'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Probabilistic models have been previously shown to be
efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the human motion model as a triangulated graph.
Previous approaches learned models based just on positions
and velocities of the body parts while ignoring their
appearance. Moreover, a heuristic approach was commonly
used to obtain translation invariance.
In this paper we suggest an improved approach for
learning such models and using them for human motion
recognition. The suggested approach combines multiple
cues, i.e., positions, velocities and appearance into both
the learning and detection phases. Furthermore, we introduce
global variables in the model, which can represent
global properties such as translation, scale or view-point.
The model is learned in an unsupervised manner from unlabelled data. We show that the suggested hybrid probabilistic model (which combines global variables, like translation, with local variables, like relative positions and appearances of body parts), leads to: (i) faster convergence of learning phase, (ii) robustness to occlusions, and, (iii) higher recognition rate