CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach
IEEE Transactions on Circuits and Systems for Video Technology
Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Monocular camera fall detection system exploiting 3d measures: a semi-supervised learning approach
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Robust fall detection by combining 3d data and fuzzy logic
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Introducing the use of depth data for fall detection
Personal and Ubiquitous Computing
Fall detection for multiple pedestrians using depth image processing technique
Computer Methods and Programs in Biomedicine
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The paper presents an active vision system for the automatic detection of falls and the recognition of several postures for elderly homecare applications. A wall-mounted Time-Of-Flight camera provides accurate measurements of the acquired scene in all illumination conditions, allowing the reliable detection of critical events. Preliminarily, an off-line calibration procedure estimates the external camera parameters automatically without landmarks, calibration patterns or user intervention. The calibration procedure searches for different planes in the scene selecting the one that accomplishes the floor plane constraints. Subsequently, the moving regions are detected in real-time by applying a Bayesian segmentation to the whole 3D points cloud. The distance of the 3D human centroid from the floor plane is evaluated by using the previously defined calibration parameters and the corresponding trend is used as feature in a thresholding-based clustering for fall detection. The fall detection shows high performances in terms of efficiency and reliability on a large real dataset in which almost one half of events are falls acquired in different conditions. The posture recognition is carried out by using both the 3D human centroid distance from the floor plane and the orientation of the body spine estimated by applying a topological approach to the range images. Experimental results on synthetic data validate the correctness of the proposed posture recognition approach.