Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Yong Chen,Mingbin Ye,Jiajia Hu,Weitong Li,Lu Wang

doi:10.1109/access.2020.3021795

Yong Chen, Mingbin Ye + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3021795

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Abstract

Fall event, as one of the greatest risks to the elderly, its detection has been a hot research issue in the solitary scene in recent years. Nevertheless, most current researches are conducted in the ideal environments, without considering the challenge of complex background in real situation. Therefore, this paper aims to detect fall event detection in complex background based on visual data. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method is first used to accurately extract the moving objects in the noise background. Then, an attention guided Bi-directional LSTM model is proposed 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.

Highlights

With the growth of the elderly population, the safety of the elders living alone becomes a rising issue for society [1]
FALL DETECTION BASED ON ATTENTION GUIDED BI-DIRECTIONAL LSTM MODEL We propose a new Bi-directional LSTM attention method for fall detection in the indoor environment, as shown in Figure 1, which is divided into three parts: Mask R-CNN layer, Bi-directional LSTM layer, and an attention layer
In order to solve these challenges, we introduce Mask R-CNN based on deep learning to replace these conventional methods, which is faster and robust

Summary

INTRODUCTION

With the growth of the elderly population, the safety of the elders living alone becomes a rising issue for society [1]. Wearable devices are growing fast, and they rely on sensors that are attached to the person’s body, such as tilt sensors, accelerometers, gyroscopes, interface pressure sensors, and magnetometers, so they are wildly used in previous works [3]–[7] These approaches have achieved high performances in fall events detection for elder care, they have to wear the sensors in daily life. Plenty of works focus on camera-based fall detection methods and performed well on existing datasets [9]–[12]. To solve the above problem, in this paper, we propose a Mask R-CNN with attention guided Bi-directional LSTM fall detection method to handle the complex background environment, which integrates the information of spatial and temporal domains in complex scenes.

RELATED WORK AND CONTRIBUTION

COMPARISON OF BACKGROUND SUBTRACTION ALGORITHMS

Findings

CONCLUSION