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Falling of elderly people who are staying alone at home leads to healthrisks. If they are not attended immediately even it may lead to fatal danger totheir life. In this paper a novel computer vision-based system for smartmonitoring of elderly people using Series Convolutional Neural Network (SCNN)with transfer learning is proposed. When CNN is trained by the frames of thevideos directly, it learns from all pixels including the background pixels.Generally, the background in a video does not contribute anything inidentifying the action and actually it will mislead the action classification.So, we propose a novel action recognition system and our contributions are 1)to generate more general action patterns which are not affected by illuminationand background variations of the video sequences and eliminate the obligationof image augmentation in CNN training 2) to design SCNN architecture andenhance the feature extraction process to learn large amount of data, 3) topresent the patterns learnt by the neurons in the layers and analyze how theseneurons capture the action when the input pattern is passing through theseneurons, and 4) to extend the capability of the trained SCNN for recognizingfall actions using transfer learning.

A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning