Human fall detection based on adaptive background mixture model and HMM
Khue Tra, Tuan Van Pham · 2013
Nowadays, there are many fall detection systems based on intelligent video analysis. However, these systems are still facing many challenges such as lighting changes, long-term scene changes or added static background objects in new scene, etc. In this paper, adaptive background Gaussian mixture model (GMM) has been applied for moving object segmentation. An ellipse shape has been built from the segmented object for body modeling. Five features are extracted from this ellipse model and fed into two Hidden Markov Models (HMM) to classify fall and normal activities. We apply our proposed approach to challenging data sets recorded in different conditions. The qualitative results demonstrate that the combination of the adaptive GMM-based object segmentation and HMM certainly improves recognition accuracy under different scenarios.