Human Activity Recognition (HAR) is popular research topic in computer vision and image processing area. Hidden Markov Models (HMMs) are used to recognize the pattern. In this paper, literature survey of different methodology and steps adapted to recognize human activities via trained Hidden Markov Model (HMM) is discussed. HMM is trained using parameters initialization of it. Parameters are initialized using feature extraction from sequence of images. Before Feature extraction image data are converted into binary or depth silhouettes. The conventional approach of features extraction from sequences of silhouetted images is using Principal Component Analysis (PCA) and novel approach is Independent Component Analysis (ICA) for HAR.

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