Machine vision
A Framework for Adaptive Sorting
SWAT '92 Proceedings of the Third Scandinavian Workshop on Algorithm Theory
ADAPTATION - Algorithms to ADAPTive FAulT MonItOriNg and Their Implementation on CORBA
DOA '01 Proceedings of the Third International Symposium on Distributed Objects and Applications
Fall Detection from Human Shape and Motion History Using Video Surveillance
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Linguistic summarization of video for fall detection using voxel person and fuzzy logic
Computer Vision and Image Understanding
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
MNFL: the monitoring and notification flow language for assistive monitoring
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Adaptive Sensor Placement and Boundary Estimation for Monitoring Mass Objects
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Estimating Daily Energy Expenditure from Video for Assistive Monitoring
ICHI '13 Proceedings of the 2013 IEEE International Conference on Healthcare Informatics
Automated In-Home Assistive Monitoring with Privacy-Enhanced Video
ICHI '13 Proceedings of the 2013 IEEE International Conference on Healthcare Informatics
Fall detection for multiple pedestrians using depth image processing technique
Computer Methods and Programs in Biomedicine
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Automated monitoring algorithms operating on live video streamed from a home can effectively aid in several assistive monitoring goals, such as detecting falls or estimating daily energy expenditure. Use of video raises obvious privacy concerns. Several privacy enhancements have been proposed such as modifying a person in video by introducing blur, silhouette, or bounding-box. Person extraction is fundamental in video-based assistive monitoring and degraded in the presence of privacy enhancements; however, privacy enhancements have characteristics that can opportunistically be adapted to. We propose two adaptive algorithms for improving assistive monitoring goal performance with privacy-enhanced video: specific-color hunter and edge-void filler. A nonadaptive algorithm, foregrounding, is used as the default algorithm for the adaptive algorithms. We compare nonadaptive and adaptive algorithms with 5 common privacy enhancements on the effectiveness of 8 automated monitoring goals. The nonadaptive algorithm performance on privacy-enhanced video is degraded from raw video. However, adaptive algorithms can compensate for the degradation. Energy estimation accuracy in our tests degraded from 90.9% to 83.9%, but the adaptive algorithms significantly compensated by bringing the accuracy up to 87.1%. Similarly, fall detection accuracy degraded from 1.0 sensitivity to 0.86 and from 1.0 specificity to 0.79, but the adaptive algorithms compensated accuracy back to 0.92 sensitivity and 0.90 specificity. Additionally, the adaptive algorithms were computationally more efficient than the nonadaptive algorithm, averaging 1.7% more frames processed per second.