Name
Abstract | ||
|---|---|---|
In many societies, the aged people are often living alone. For the aging population, surveillance in household environments has become more and more important. In this paper, we present a household surveillance robot that can detect abnormal events by utilizing video and audio information. In our approach, moving targets can be detected by the robot with a passive acoustic location device. Then the robot tracks the targets by employing a particle filter algorithm. In adapting to different lighting conditions, the target model is updated regularly based on an update mechanism. To achieve robust tracking, the robot detects abnormal human behaviors by tracking the upper body of a person. For audio surveillance, Mel frequency cepstral coefficients(MFCC) is used to extract features from audio information. Those features are input to a support vector machine classifier for analysis. When any abnormal audio information is detected, a camera on the robot will be triggered to further confirm the occurrence of the abnormal event. Experimental results show that the robot can detect abnormal behaviors such as ldquofalling downrdquo and ldquorunningrdquo. Also, a 88.17% accuracy rate is achieved in the detection of abnormal audio information like ldquocryingrdquo, ldquogroanrdquo, and ldquogun shootingrdquo. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ROBIO.2009.4913263 | ROBIO |
Keywords | Field | DocType |
abnormal human behaviors,particle filtering (numerical methods),intelligent household surveillance robot,household surveillance robot,abnormal human behavior,audio information,particle filter algorithm,home automation,aging population,mel frequency cepstral coefficient,service robots,moving targets detection,audio surveillance,passive acoustic location device,feature extraction,abnormal event,intelligent robots,robust tracking,abnormal events detection,abnormal audio information,mel frequency cepstral coefflcients,abnormal behavior,household environment,robot vision,video surveillance,support vector machine,human behavior,particle filter | Mel-frequency cepstrum,Computer vision,Support vector machine classifier,Intelligent robots,Home automation,Speech recognition,Particle filtering algorithm,Human behavior,Artificial intelligence,Engineering,Acoustic location,Robot | Conference |
ISBN | Citations | PageRank |
978-1-4244-2679-9 | 5 | 0.48 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyu Wu | 1 | 515 | 80.44 |
| Haitao Gong | 2 | 19 | 2.36 |
| Pei Chen | 3 | 86 | 15.51 |
| Zhi Zhong | 4 | 17 | 3.12 |
| Yangsheng Xu | 5 | 1541 | 245.29 |