Vision-Based Fall Detection with Convolutional Neural Networks

Abstract

One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

Depth and thermal information fusion for head tracking using particle filter in a fall detection context

“…Wang et al [23] proposed a fall detection system using a PCAnet to extract features from color images and then applied a SVM to detect falls. Nunez-Marcos et al [24] proposed a similar approach but, instead of a PCAnet, they used a modified VGG16 architecture. These methods are promising but usually require a large dataset to train a classifier and are inclined to be influenced by the image quality.…”

An Empirical Study Towards Understanding How Deep Convolutional Nets Recognize Falls

“…Although several relevant methods like [24] have been proposed, the underlying reasons of the effectiveness are still not clear. In this paper, rather than proposing a novel method for fall recognition, we aim at attaining insights of how the deep convolutional net recognizes falls via a series of empirical investigations.…”

Vision-Based Human Action Classification Using Adaptive Boosting Algorithm

“…Wang et al proposed neural network and SVM classifiers for recognition based on both histograms of oriented gradients (HOG) and local binary pattern (LBP) [20]. Nunez et al proposed a vision-based solution using convolutional neural networks to detect if a video sequence contains fall incidents [21]. Indeed, human behavior understanding remains a challenging task because of the complexity of monitored environments, which are becoming more and more complicated and crowded.…”