Multimedia Tools and Applications

, Volume 75, Issue 19, pp 11603–11613 | Cite as

Human fall detection in surveillance video based on PCANet

  • Shengke Wang
  • Long Chen
  • Zixi Zhou
  • Xin Sun
  • Junyu Dong


Fall incidents have been reported as the second most common cause of death, especially for elderly people. Human fall detection is necessary in smart home healthcare systems. Recently various fall detection approaches have been proposed., among which computer vision based approaches offer a promising and effective way. In this paper, we proposed a new framework for fall detection based on automatic feature learning methods. First, the extracted frames, including human from video sequences of different views, form the training set. Then, a PCANet model is trained by using all samples to predict the label of every frame. Because a fall behavior is contained in many continuous frames, the reliable fall detection should not only analyze one frame but also a video sequence. Based on the prediction result of the trained PCANet model for each frame, an action model is further obtained by SVM with the predicted labels of frames in video sequences. Experiments show that the proposed method achieved reliable results compared with other commonly used methods based on the multiple cameras fall dataset, and a better result is further achieved in our dataset which contains more training samples.


Fall detection PCANet Behavior analysis Patient monitoring Visual surveillance 



This work is supported by the National Natural Science Foundation of China (NSFC) Grants 61301241, 61401413, 61403353 and 61271405.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Shengke Wang
    • 1
  • Long Chen
    • 1
  • Zixi Zhou
    • 1
  • Xin Sun
    • 1
  • Junyu Dong
    • 1
  1. 1.Ocean University of ChinaQingdaoChina

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