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Human motion recognition in video data has several interesting applications in fields such as gaming, senior/assisted living environments, and surveillance. In these scenarios, we might have to consider adding new motion classes (i.e. new types of human motions to be recognized) as well as new training data (say, for handling different type of subjects). Hence, both accuracy of classification and training time for the machine learning algorithms become important performance parameters in these cases. In this paper, we propose a Knowledge Based Hybrid (KBH) method that can compute the probabilities for Hidden Markov Models (HMMs) associated with different human motion classes. This computation is facilitated by appropriately mixing features from two different media types (3D motion capture and 2D video). We conducted a variety of experiments comparing the proposed KBH for HMMs and the traditional Baum-Welch algorithms. With the advantage of computing the HMMs parameters in a non-iterative manner, the KBH method outperforms the Baum-Welch algorithm both in terms of accuracy as well as reduced training time.