automated fall detection on privacy-enhanced video alex edgcomb frank vahid university of...
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Automated Fall Detection on Privacy-Enhanced Video
Alex EdgcombFrank Vahid
University of California, RiversideDepartment of Computer Science
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Reasons to detect falls with privacy-enhanced video
Privacyadjustable
Detect otherevents
Body-worn
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+Anywhere
-Not always worn
Efficient person-detection in video
Background image Video frame Foreground
=-
via foreground-background segmentation
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Abstracting person to rectangle
Video frame
Foreground
Minimum bounding rectangle
(MBR) of foreground
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Fall shown as four MBR features
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Fall classification (details in paper)
Observed shape
Characteristicfall shape
Similarity0.84
Dynamic time warping
Non-fall
Non-fall Fall
Observed shape
0.46
0.88
Binary tree classification
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DTW established time series technique
Recordings gathered
• 23 recordings (12 fall, 11 non-fall)• Sole male twenty-six year old actor• Recorded in living room• Recorded with webcam @ 15 fps
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Fall detection accuracy by featureFeature Average sensitivity Average specificity
Height of MBR in pixels
0.31 0.30Width of MBR in
pixels 0.91 0.92Height-to-width
ratio of MBR0.44 0.50
Width-to-height ratio of MBR
0.64 0.67
For each feature, trained binary classifier using leave-one-video-out, then tested with video left out.
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Fall detection on privacy-enhanced video
Raw Blur Silhouette Bounding-oval
Bounding-box
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Fall detection accuracy by privacy enhancement
Privacy setting Average sensitivity Average specificity
Raw 0.91 0.92Blur 1.00 0.67
Silhouette 0.91 0.75
Bounding-oval 0.91 0.92Bounding-box 0.82 0.92
• Auto-converted 23 raw videos into each privacy enhancement• Used trained binary classifier from raw video.• Tested with each privacy enhancement.
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Characteristic fall shape is nearly identical for raw and privacy-enhanced video
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Conclusions
• Bounding-oval yielded same accuracy as raw video
• Privacy-enhanced fall detection is viable
• Future work– Compare our algorithm to
previous works– Experiments with more recordings– Consider more privacy
enhancementsCopyright © 2012 Alex Edgcomb, UC Riverside. 12 of 12