  Fall event detection, as one of the greatest risks to the elderly, has been a
hot research issue in the solitary scene in recent years. Nevertheless, there
are few researches on the fall event detection in complex background. Different
from most conventional background subtraction methods which depend on
background modeling, Mask R-CNN method based on deep learning technique can
clearly extract the moving object in noise background. We further propose an
attention guided Bi-directional LSTM model for the final fall event detection.
To demonstrate the efficiency, the proposed method is verified in the public
dataset and self-build dataset. Evaluation of the algorithm performances in
comparison with other state-of-the-art methods indicates that the proposed
design is accurate and robust, which means it is suitable for the task of fall
event detection in complex situation.
