Hierarchical multi-observation model for multi-person activity recognition
Youtian Du · Journal of Tsinghua University(Science and Technology) · 2009
A recursive multi-level stochastic model is presented for multi-person activity recognition, such as in a high-dimension feature space, role assignments and with complex temporal structures. The model represents the multi-scale characteristics of activities by the multi-level network and captures the long-term dependency using high-level chains. The model decomposing observations considerably reduce the dimensionality of the feature space. The assumption that sub-observations have uniform distributions partly eliminates the effects of role assignment errors. Test results demonstrate that the model outperforms other popular models, has 91.3% recognition rate even for complex activities.