Name
Abstract | ||
|---|---|---|
Video Surveillance is an omnipresent topic when it comes to enhancing security and safety in the intelligent home environments. In this paper we propose a novel method to detect various posture-based events in a typical elderly monitoring application in a home surveillance scenario. These events include normal daily life activities, abnormal behaviors and unusual events. Due to the fact that falling and its physical-psychological consequences in the elderly are a major health hazard, we monitor human activities with a particular interest to the problem of fall detection. Combination of best-fit approximated ellipse around the human body, horizontal and vertical velocities of movement and temporal changes of centroid point, would provide a useful cue for detection of different behaviors. Extracted feature vectors are finally fed to a fuzzy multiclass support vector machine for precise classification of motions and determination of a fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-90-481-3656-8_95 | TECHNOLOGICAL DEVELOPMENTS IN EDUCATION AND AUTOMATION |
Keywords | Field | DocType |
support vector machine,feature vector,human body | Computer vision,Horizontal and vertical,Feature vector,Pattern recognition,Health hazard,Computer science,Support vector machine,Fuzzy logic,Artificial intelligence,Ellipse,Human body,Centroid | Conference |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Homa Foroughi | 1 | 31 | 3.65 |
| Mohamad Alishahi | 2 | 1 | 0.35 |
| Hamidreza Pourreza | 3 | 28 | 3.10 |
| Maryam Shahinfar | 4 | 1 | 0.35 |