Human action recognition (HAR) from videos is a significant and has more research focus in the domain of Computer vision. The purpose of human action recognition in videos is to detect and recognize the human actions from the sequence of frames. Human action recognition undertakes many difficulties such as differences in human shape, cluttered background, moving cameras, illumination conditions, motion, occlusion, and viewpoint variations. In previously, local features or deep learned features are used to recognize the action. In the proposed work, both the features are used to recognize action and for analysis. From sequences of frames background is subtracted using Multi-frame averaging method. Two kinds of feature extraction are done. Shape based feature extraction, Optical flow feature extraction are some of the hand-crafted features performed and classification is done using HMM. The other one is deep learned features. Convolutional Neural Network extracts the features from frames in each layer. It extracts the features such as line, edge, color, texture and Classification is done using SVM. For human action recognition, hand-crafted features attain good result but it fails on large set of data. Deep learned features such as CNN have been used for large dataset and good result is obtained on recognition. To improve the human action recognition result, CNN is proposed. We compared both the approaches CNN and HMM and the results were analyzed. CNN results better accuracy while comparing with HMM.

Background Subtraction, Convolutional Neural Network, Canny Edge Detection, Optical Flow, Hidden Markov Model

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