Real-Time fall detection method based on hidden markov modelling
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
| Hi-index | 0.00 |
In this paper, we designed a simple and fast visual surveillance system to track human position and to determine if any abnormal behavior like wall climbing and falling happened. By taking both time and background difference into considerations, illumination effects could be greatly reduced while calculating motion masks. Refinements including holes filling, shadow removal, and noise reduction are done to obtain much more reliable motion masks. However, motion masks corresponding to occluded moving people, greater than a given width, are segmented recursively into smaller ones by bi-modal thresholding. Meanwhile, background could also be updated by the refined motion masks. Integrated location-based and weighted block-based matching is done for object tracking. A similarity is defined from these weighted matched block for object classification. Finally, a couple of criterions are defined to analyze whether objects stop, disappear, climb, or fall. Experimental results are given to demonstrate the robustness of our system.