Representation and Optimal Recognition of Human Activities
Towards the goal of realizing a generic automatic human
activity recognition system, a new formalism is proposed.
Activities are described by
a chained hierarchical representation using three type of
entities: image features, mobile object properties
Taking image features of tracked moving regions
from an image sequence as input,
mobile object properties are
first computed by specific methods while noise is
suppressed by statistical methods.
Scenarios are recognized from mobile object properties
based on Bayesian analysis.
A sequential occurrence several scenarios are
recognized by an algorithm using a probabilistic
finite-state automaton (a variant of structured HMM).
Finally, the validity and the effectiveness of
our approach is demonstrated on both
real-world and perturbed data.
Video Surveillance And Monitoring