Abstract
In this paper, we present an approach for human fall detection, which has important applications in the field of safety and security. The proposed approach consists of two parts: object detection and the use of a fall model. We use an adaptive background subtraction method to detect a moving object and mark it with its minimum-bounding box. The fall model uses a set of extracted features to analyze, detect and confirm a fall. We implement a two-state finite state machine (FSM) to continuously monitor people and their activities. Experimental results show that our method can detect most of the possible types of single human falls quite accurately.
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© 2007 Springer-Verlag Berlin Heidelberg
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Vishwakarma, V., Mandal, C., Sural, S. (2007). Automatic Detection of Human Fall in Video. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_76
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DOI: https://doi.org/10.1007/978-3-540-77046-6_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77045-9
Online ISBN: 978-3-540-77046-6
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