  In computer vision, action recognition refers to the act of classifying an
action that is present in a given video and action detection involves locating
actions of interest in space and/or time. Videos, which contain photometric
information (e.g. RGB, intensity values) in a lattice structure, contain
information that can assist in identifying the action that has been imaged. The
process of action recognition and detection often begins with extracting useful
features and encoding them to ensure that the features are specific to serve
the task of action recognition and detection. Encoded features are then
processed through a classifier to identify the action class and their spatial
and/or temporal locations. In this report, a thorough review of various action
recognition and detection algorithms in computer vision is provided by
analyzing the two-step process of a typical action recognition and detection
algorithm: (i) extraction and encoding of features, and (ii) classifying
features into action classes. In efforts to ensure that computer vision-based
algorithms reach the capabilities that humans have of identifying actions
irrespective of various nuisance variables that may be present within the field
of view, the state-of-the-art methods are reviewed and some remaining problems
are addressed in the final chapter.
