Abstract
In the background of global aging, more attention should be paid to the elders’ health and the equality of their life. Nowadays falls became one of greatest danger for old people. Almost 62% of injury-related hospitalizations for the old are the result of it. In this paper, we propose a new method to detect fall based on judging human’s moving posture from the video. It consists of three main parts, detecting the moving object, extracting the feature and recognizing the pattern of behavior. To improve the precision and increase the speed of the detection, we adopt two layers codebook background modeling and codebook fragmentation training. Two level SVM method to recognize the behavior: In the first level of the SVM classifier, we distinguish the standing posture and other posture by the feature of moving object, such as the ratio of the major and minor axis of the ellipse. In the second level of the SVM classifier, angle of the ellipse and head moving trajectory to judge the falls and squat. The experimental results indicate that our system can detect fall effectively.
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Zhao, K., Jia, K., Liu, P. (2017). Fall Detection Algorithm Based on Human Posture Recognition. In: Pan, JS., Tsai, PW., Huang, HC. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-319-50212-0_15
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DOI: https://doi.org/10.1007/978-3-319-50212-0_15
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