Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow

TL;DR

This paper introduces an enhanced optical flow method using rank pooling to improve accuracy and processing speed of vision-based fall detection in complex lighting environments, aiding elderly safety.

Contribution

It proposes a novel Enhanced Dynamic Optical Flow technique that improves fall detection accuracy and reduces processing time in vision-based systems under challenging conditions.

Findings

Classification accuracy increased by around 3%

Processing time reduced by 40-50ms

Improved pre-processing with dynamic optical flow

Abstract

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.