| The ability to predict the intentions of people based solely on the visual observations of people's actions is a skill only performed by humans and animals. This requires segmentation of items in the field of view, tracking of moving objects, identifying the importance of each object, determining the current role of each important object individually and in collaboration with other objects, relating these objects into a predefined scenario, assessing the selected scenario with the information retrieve, and finally adjusting the scenario to better fit the data. This is all accomplished with great accuracy in less than a few seconds.;The intelligence of current computer algorithms has not reached this level of complexity with the accuracy and time constraints that humans and animals have, but there are several research efforts that are working towards this by identifying new algorithms for solving parts of this problem.;This research concentrates on the development of a new method for identifying the role, or action, of a specific object, a human object. This new classification algorithm, named Evidence Feed Forward Hidden Markov Model (HMM), can then be implemented into a system meant to predict the intentions of a person; which is, of course, based mainly on a person's current actions and the surrounding environment. The Evidence Feed Forward HMM can also be used in several other types of classification problems, but it is mainly developed for the problems involving sequences of actions and relations from an observation to an observation.;The Evidence Feed Forward HMM 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 the learning process to update model parameters to optimize the likelihood of observations with the Evidence Feed Forward HMM, 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. |
