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Multi-view fall detection based on spatio-temporal interest points

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Abstract

Many countries are experiencing a rapid increase in their elderly populations, increasing the demand for appropriate healthcare systems including fall-detection systems. In recent years, many fall-detection systems have been developed, although most require the use of wearable devices. Such systems function only when the subject is wearing the device. A vision-based system presents a more convenient option. However, visual features typically depend on camera view; a single, fixed camera may not properly identify falls occurring in various directions. Thus, this study presents a solution that involves using multiple cameras. The study offers two main contributions. First, in contrast to most vision-based systems that analyze silhouettes to detect falls, the present system proposes a novel feature for measuring the degree of impact shock that is easily detectable with a wearable device but more difficult with a computer vision system. In addition, the degree of impact shock is less sensitive to camera views and can be extracted more robustly than a silhouette. Second, the proposed method uses a majority-voting strategy based on multiple views to avoid performing the tedious camera calibration required by most multiple-camera approaches. Specifically, the proposed method is based on spatio-temporal interest points (STIPs). The number of local STIP clusters is designed to indicate the degree of impact shock and body vibration. Sequences of these features are concatenated into feature vectors that are then fed into a support vector machine to classify the fall event. A majority-voting strategy based on multiple views is then used for the final determination. The proposed method has been applied to a publicly available dataset to offer evidence that the proposed method outperforms existing methods based on the same data input.

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Acknowledgments

The authors would like to thank E. Auvinet, C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau for providing “Multiple cameras fall dataset,” and anonymous reviewers for the valuable and insightful comments on the earlier version of this manuscript. This work was supported by the National Science Council of Taiwan, Republic of China (NSC-103-2221-E-155-033-), the Nature Science Foundation of China (No. 61202143), and the Natural Science Foundation of Fujian Province (No. 2013J05100).

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Authors and Affiliations

  1. School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China

    Songzhi Su & Shaozi Li

  2. Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, Fujian, China

    Songzhi Su & Shaozi Li

  3. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan

    Sin-Sian Wu & Shu-Yuan Chen

  4. Department of Computer Science and Information Engineering, Chien Hsin University of Science and Technology, Taoyuan, Taiwan

    Der-Jyh Duh

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  1. Songzhi Su
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Correspondence to Shu-Yuan Chen or Shaozi Li.

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Su, S., Wu, SS., Chen, SY. et al. Multi-view fall detection based on spatio-temporal interest points. Multimed Tools Appl 75, 8469–8492 (2016). https://doi.org/10.1007/s11042-015-2766-3

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  • DOI: https://doi.org/10.1007/s11042-015-2766-3

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