The ability to predict the intentions of people based solely on their visual
actions is a skill only performed by humans and animals. The intelligence of
current computer algorithms has not reached this level of complexity, but there
are several research efforts that are working towards it. With the number of
classification algorithms available, it is hard to determine which algorithm
works best for a particular situation. In classification of visual human intent
data, Hidden Markov Models (HMM), and their variants, are leading candidates.
The inability of HMMs to provide a probability in the observation to
observation linkages is a big downfall in this classification technique. If a
person is visually identifying an action of another person, they monitor
patterns in the observations. By estimating the next observation, people have
the ability to summarize the actions, and thus determine, with pretty good
accuracy, the intention of the person performing the action. These visual cues
and linkages are important in creating intelligent algorithms for determining
human actions based on visual observations.
The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm
which provides observation to observation linkages. The following research
addresses the theory behind Evidence Feed Forward HMMs, provides mathematical
proofs of their learning of these parameters to optimize the likelihood of
observations with a Evidence Feed Forwards HMM, which is important in all
computational intelligence algorithm, and gives comparative examples with
standard HMMs in classification of both visual action data and measurement
data; thus providing a strong base for Evidence Feed Forward HMMs in
classification of many types of problems.