Camera-based fall detection using a particle filter

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. The lack of timely aid after such a fall incident can lead to severe complications. This timely aid can however be assured by a camera-based fall detection system triggering an alarm w...

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Vydáno v:2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Ročník 2015; s. 6947 - 6950
Hlavní autoři: Debard, Glen, Baldewijns, Greet, Goedeme, Toon, Tuytelaars, Tinne, Vanrumste, Bart
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2015
Témata:
Accidental Falls
Aged
Algorithms
Atmospheric measurements
Detection algorithms
Feature extraction
Filtration - instrumentation
Histograms
Humans
Image color analysis
Photography - instrumentation
Sensitivity
Tracking
ISSN:1094-687X, 1557-170X, 2694-0604, 2694-0604
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Shrnutí:More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. The lack of timely aid after such a fall incident can lead to severe complications. This timely aid can however be assured by a camera-based fall detection system triggering an alarm when a fall occurs. Most algorithms described in literature use the biggest object detected using background subtraction to extract the fall features. In this paper we compare the performance of our state-of-the-art fall detection algorithm when using only background subtraction, when using a particle filter to track the person and a hybrid method in which the particle filter is only used to enhance the background subtraction and not for the feature extraction. We tested this using our simulation data set containing reenactments of real-life falls. This comparison shows that this hybrid method significantly increases the sensitivity and robustness of the fall detection algorithm resulting in a sensitivity of 76.1% and a PPV of 41.2%.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1094-687X
1557-170X
2694-0604
2694-0604
DOI:10.1109/EMBC.2015.7319990