Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation
AbstractThis paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD). Furthermore, we have developed a histogram-based representation (HBR) based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine) classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Alazrai, R.; Momani, M.; Daoud, M.I. Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation. Appl. Sci. 2017, 7, 316.
Alazrai R, Momani M, Daoud MI. Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation. Applied Sciences. 2017; 7(4):316.Chicago/Turabian Style
Alazrai, Rami; Momani, Mohammad; Daoud, Mohammad I. 2017. "Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation." Appl. Sci. 7, no. 4: 316.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.