Video modeling using 3-D hidden markov model

Statistical modeling methods have become critical for many image processing problems, such as segmentation, compression and classification. In this paper we are proposing and experimenting a computationally efficient simplification of 3-Dimensional Hidden Markov Models. Our proposed model relaxes the dependencies between neighboring state nodes to a random uni-directional dependency by introducing a three dimensional dependency tree (3D-DT HMM). To demonstrate the potential of the model we apply it to the problem of tracking objects in a video sequence. We explore various issues about the effect of the random tree and smoothing techniques. Experiments demonstrate the potential of the model as a tool for tracking video objects with an efficient computational cost.