Falling of elderly people who are staying alone at home leads to health
risks. If they are not attended immediately even it may lead to fatal danger to
their life. In this paper a novel computer vision-based system for smart
monitoring of elderly people using Series Convolutional Neural Network (SCNN)
with transfer learning is proposed. When CNN is trained by the frames of the
videos directly, it learns from all pixels including the background pixels.
Generally, the background in a video does not contribute anything in
identifying 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 illumination
and background variations of the video sequences and eliminate the obligation
of image augmentation in CNN training 2) to design SCNN architecture and
enhance the feature extraction process to learn large amount of data, 3) to
present the patterns learnt by the neurons in the layers and analyze how these
neurons capture the action when the input pattern is passing through these
neurons, and 4) to extend the capability of the trained SCNN for recognizing
fall actions using transfer learning.
L. Aneesh Euprazia, K. K. Thyagharajan