Recovering Human Motion Tracking System using Gaussian Process

Estimation of motions and tracking the motions with the help of a motion tracking system has been considered major dilemma due to the availability of various frameworks. In the process of tracking, we need to form a mapping from observation space to the state space. We have reviewed Gaussian Process with both discriminative and generative frameworks for the purpose of motion estimation. Both the input and output are shown at the same place. But there are certain limitations due to the dilemma of multimodality. To overcome these limitations we have used a mix of Gaussian process experts. In this paper, we have combined acts as a fusion of experts. We have used Gaussian process, dynamic model for learning the movements in latent state space. We had not only estimated human motions but also tracked motions with respe area of study discussed in this paper would be useful to trace out physical and behavioral patterns of living and non living object and this would help in studying the forecast patterns such as natural blooming, industry tool des earthquakes forecast , monitoring and in control applications along with their impact so as to offer corrective measures before hand.