  Accurate fall detection for the assistance of older people is crucial to
reduce incidents of deaths or injuries due to falls. Meanwhile, a vision-based
fall detection system has shown some significant results to detect falls.
Still, numerous challenges need to be resolved. The impact of deep learning has
changed the landscape of the vision-based system, such as action recognition.
The deep learning technique has not been successfully implemented in
vision-based fall detection systems due to the requirement of a large amount of
computation power and the requirement of a large amount of sample training
data. This research aims to propose a vision-based fall detection system that
improves the accuracy of fall detection in some complex environments such as
the change of light condition in the room. Also, this research aims to increase
the performance of the pre-processing of video images. The proposed system
consists of the Enhanced Dynamic Optical Flow technique that encodes the
temporal data of optical flow videos by the method of rank pooling, which
thereby improves the processing time of fall detection and improves the
classification accuracy in dynamic lighting conditions. The experimental
results showed that the classification accuracy of the fall detection improved
by around 3% and the processing time by 40 to 50ms. The proposed system
concentrates on decreasing the processing time of fall detection and improving
classification accuracy. Meanwhile, it provides a mechanism for summarizing a
video into a single image by using a dynamic optical flow technique, which
helps to increase the performance of image pre-processing steps.
