Human Fall Detection using Co-Saliency-Enhanced Deep Recurrent Convolutional Neural Networks
Journal article, 2019

This paper addresses issues of fall detection from videos for e-healthcare and assisted-living. Instead of using hand-crafted features from videos, we exploit a dedicated recurrent convolutional network (RCN) architecture for fall detection in combination with co-saliency enhancement. In the proposed scheme, the recurrent neural network (RNN) is realized by Long Short-Term Memory (LSTM) connecting to a set of Convolutional Neural Networks (CNNs), where each video is modelled as an ordered sequence, containing several frames. In such a way, the sequential information in video is preserved. To further enhance the performance, we propose to employ co-saliency-enhanced video frames as the inputs of RCN, where salient human activity regions are enhanced. Experimental results have shown that the proposed scheme is effective. Further, our results have shown very good test performance (accuracy 98.12%), and employing the co-saliency-enhanced RCN has led to the improvement in performance (0.70% on test) as comparing to that without co-saliency. Comparisons with two existing methods have provided further support to effectiveness of the proposed scheme.

recurrent convolutional network

human fall detection

Long Short-Term Memory

co-saliency enhancement.



Chenjie Ge

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Jie Yang

Shanghai Jiao Tong University

Internationa Research Journal of Engineering and Technology (IRJET)

2395-0056 (eISSN)

Vol. 6 9 993-1000

Areas of Advance

Health Engineering

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

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