Personalized multimedia alert service of fall event for ageing in place
Proceedings of the First International Conference on Internet Multimedia Computing and Service
An active vision system for fall detection and posture recognition in elderly healthcare
Proceedings of the Conference on Design, Automation and Test in Europe
Introducing a statistical behavior model into camera-based fall detection
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Slip and fall event detection using Bayesian Belief Network
Pattern Recognition
Fuzzy sets for human fall pattern recognition
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Camera-Based fall detection on real world data
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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
Fall detection in multi-camera surveillance videos: experimentations and observations
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Fall detection for multiple pedestrians using depth image processing technique
Computer Methods and Programs in Biomedicine
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Automatic detection of a falling person in video sequences has interesting applications in video-surveillance and is an important part of future pervasive home monitoring systems. In this paper, we propose a multiview approach to achieve this goal, where motion is modeled using a layered hidden Markov model (LHMM). The posture classification is performed by a fusion unit, merging the decision provided by the independently processing cameras in a fuzzy logic context. In each view, the fall detection is optimized in a given plane by performing a metric image rectification, making it possible to extract simple and robust features, and being convenient for real-time purpose. A theoretical analysis of the chosen descriptor enables us to define the optimal camera placement for detecting people falling in unspecified situations, and we prove that two cameras are sufficient in practice. Regarding event detection, the LHMM offers a principle way for solving the inference problem. Moreover, the hierarchical architecture decouples the motion analysis into different temporal granularity levels, making the algorithm able to detect very sudden changes, and robust to low-level steps errors.