“HUMAN ACTION RECOGNITION SYSTEM USING MATLAB"
Mr. Dhananjay A. Deshpande, Prof. Snehil G. Jaiswal
MTech Student, Department of Electronics and Telecommunication, G. H. RAISONI University, Amravati, Maharashtra, India
Assistant Professor, Department of Electronics and Telecommunication, G. H. RAISONI University, Amravati, Maharashtra, India
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Abstract – Human actions detection is very much investigated in utilization of artificial intelligence and computer vision. Numerous effective action recognition strategies have demonstrated and the action information are successfully gained from motion videos and still pictures. In order to get the equivalent actions, the proper activity information gained from various kinds of media like videos or pictures might be connected. The majority of the existing video activity action identification strategies experience the ill effects of inadequate marked recordings. In that cases, over-fitting should be a potential issue and the execution of activity acknowledgment is controlled. In this paper, image processing techniques are used in order to recognize the different hand poster of the human body, also the over-fitting can be eased and the execution of activity acknowledgment is improved. Initially, the human action video including hand waving, walking, jogging, clapping, boxing is converted into image of 2D frames and then it is pre-processed followed by feature extraction using LST and classification by KNN classifier has been done individually. The kernel principal component analysis (KPCA) technique is used in the proposed system for finding the image features and joined features. The extracted features from the frames are compared with trained quantized dataset in order to identify the actions. The advantage of quantized dataset is that it occupies very less space. Thus, the result shows which action is present in the examined data. Trials on open benchmark data sets and genuine world data sets demonstrate that our technique outflanks a few other cutting edge activity acknowledgment strategies.
Key Words: Action Recognition System, Convolutional Neural Network (CNN), Human Activity Recognition (HAR), Deep Learning (DL), Machine Learning (ML), 3D CNN.
