Automated fall detection on privacy-enhanced video.
A privacy-enhanced video obscures the appearance of a person in the video. We consider four privacy enhancements: blurring of the person, silhouetting of the person, covering the person with a graphical box, and covering the person with a graphical oval. We demonstrate that an automated video-based fall detection algorithm can be as accurate on privacy-enhanced video as on raw video. The algorithm operated on video from a stationary in-home camera, using a foreground-background segmentation algorithm to extract a minimum bounding rectangle (MBR) around the motion in the video, and using time series shapelet analysis on the height and width of the rectangle to detect falls. We report accuracy applying fall detection on 23 scenarios depicted as raw video and privacy-enhanced videos involving a sole actor portraying normal activities and various falls. We found that fall detection on privacy-enhanced video, except for the common approach of blurring of the person, was competitive with raw video, and in particular that the graphical oval privacy enhancement yielded the same accuracy as raw video, namely 0.91 sensitivity and 0.92 specificity.
Department of Computer Science and Engineering, University of California, Riverside, CA 92507, USA. aedgcomb@cs.ucr.edu
Alex
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Edgcomb
Frank
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Vahid
D000058:Accidental Falls; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods*; D012680:Sensitivity and Specificity; D014741:Video Recording