A normalized scoring algorithm has been developed for Hidden Markov Models (HMMs) to establish independent individual-model evaluation of each input sequence. Using this model, it has become possible for each trained HMM to judge if an input sequence is classified to the category of the model by a simple thresholding operation without referring to other models. Such evaluation has been enabled by creating a self model for each input sequence by on-line learning. As a result, a long action sequence composed of unit-motions can be recognized using multiple models each trained for each unit-motion. The algorithm was evaluated in 120 test sessions and the recognition rates of average 92.3% and 85.3% for unit motion detection and entire sequence recognition, respectively, have been demonstrated.
An HMM-based motion perception system capable of identifying individual motion of multiple objects present in a scene has been developed. By introducing a new gridpartitioning scheme representing the characteristic motions in the scene, translation invariant perception has been established. An autonomous time indexing scheme has also been developed and implemented in the system. Recognition experiments were carried out for simple arm moving patterns in a variety of environments including translation and scaling of object images as well as the mixture of different patterns in a single scene. The average recognition accuracy of above 81% has been achieved in each experiments.
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