Automated Fall Detection on Privacy-Enhanced Video Alex Edgcomb Frank Vahid University of California, Riverside Department of Computer Science Copyright.

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Presentation transcript:

Automated Fall Detection on Privacy-Enhanced Video Alex Edgcomb Frank Vahid University of California, Riverside Department of Computer Science Copyright © 2012 Alex Edgcomb, UC Riverside.1 of 12

Reasons to detect falls with privacy- enhanced video Privacy adjustable Detect other events Body-worn Copyright © 2012 Alex Edgcomb, UC Riverside.2 of 12 +Anywhere -Not always worn

Efficient person-detection in video Background imageVideo frameForeground =- via foreground-background segmentation Copyright © 2012 Alex Edgcomb, UC Riverside.3 of 12

Abstracting person to rectangle Video frame Foreground Minimum bounding rectangle (MBR) of foreground Copyright © 2012 Alex Edgcomb, UC Riverside.4 of 12

Fall shown as four MBR features Copyright © 2012 Alex Edgcomb, UC Riverside.5 of 12

Fall classification (details in paper) Observed shape Characteristic fall shape Similarity 0.84 Dynamic time warping Non-fall Fall Observed shape Binary tree classification Copyright © 2012 Alex Edgcomb, UC Riverside.6 of 12 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 15 fps Copyright © 2012 Alex Edgcomb, UC Riverside.7 of 12

Fall detection accuracy by feature FeatureAverage sensitivityAverage specificity Height of MBR in pixels Width of MBR in pixels Height-to-width ratio of MBR Width-to-height ratio of MBR For each feature, trained binary classifier using leave-one-video- out, then tested with video left out. Copyright © 2012 Alex Edgcomb, UC Riverside.8 of 12

Fall detection on privacy-enhanced video Raw Blur Silhouette Bounding -oval Bounding -box Copyright © 2012 Alex Edgcomb, UC Riverside.9 of 12

Fall detection accuracy by privacy enhancement Privacy settingAverage sensitivityAverage specificity Raw Blur Silhouette Bounding-oval Bounding-box Auto-converted 23 raw videos into each privacy enhancement Used trained binary classifier from raw video. Tested with each privacy enhancement. Copyright © 2012 Alex Edgcomb, UC Riverside.10 of 12

Characteristic fall shape is nearly identical for raw and privacy-enhanced video Copyright © 2012 Alex Edgcomb, UC Riverside.11 of 12

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 enhancements Copyright © 2012 Alex Edgcomb, UC Riverside.12 of 12