Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Features for Word Spotting in Historical Manuscripts
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
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A Hidden Markov Model (HMM) is used as an efficient and robust technique for human activities classification. The HMM evaluates a set of video recordings to classify each scene as a function of the future, actual and previous scenes. The probabilities of transition between states of the HMM and the observation model should be adjusted in order to obtain a correct classification. In this work, these matrixes are estimated using the well known Baum-Welch algorithm that is based on the definition of the real observations as a mixture of two Gaussians for each state. The application of the GA follows the same principle but the optimization is carried out considering the classification. In this case, GA optimizes the Gaussian parameters considering as a fitness function the results of the classification application. Results show the improvement of GA techniques for human activities recognition.