W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Smart Sensor to Detect the Falls of the Elderly
IEEE Pervasive Computing
Activity Summarisation and Fall Detection in a Supportive Home Environment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
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
Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Modeling human activity from voxel person using fuzzy logic
IEEE Transactions on Fuzzy Systems
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
HMM based falling person detection using both audio and video
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Probabilistic posture classification for Human-behavior analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach
IEEE Transactions on Circuits and Systems for Video Technology
Fall detection for multiple pedestrians using depth image processing technique
Computer Methods and Programs in Biomedicine
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This paper proposes a method to detect slip-only events and fall events based on the motion activity measure and human silhouette shape variations. Here, we also apply the Bayesian Belief Network (BBN) to model the causality of the events before and after the fall and slip-only events. The motion measure is obtained by analyzing the energy of the motion active (MA) area in the integrated spatiotemporal energy (ISTE) map. Unlike the motion history image (MHI), the ISTE map can be applied to detect fall and slip-only events. The contributions of this study are: (a) proposing the ISTE map; (b) detecting the fall parallel to the optical axis; (c) application to non-fixed frame rate video; (d) identifying the slip-only event; and (e) using BBN to model the causality of the slip or fall events with other events. Early identification of a slip-only event can help prevent falls and injuries. In the experiments, we demonstrate that our method is effective in detecting both fall and slip-only events.