Surveillance Robot Utilizing Video and Audio Information
For the aging population, surveillance in household environments has become more and more important. In this paper, we present a household robot that can detect abnormal events by utilizing video and audio information. In our approach, moving targets can be detected by the robot using a passive acoustic location device. The robot then tracks the targets by employing a particle filter algorithm. To adapt to different lighting conditions, the target model is updated regularly based on an update mechanism. To ensure robust tracking, the robot detects abnormal human behavior 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. Experimental results show that the robot can detect abnormal behavior such as "falling down" and "running". Also, a 88.17% accuracy rate is achieved in the detection of abnormal audio information like "crying", "groan", and "gun shooting". To lower the false alarms by abnormal sound detection system, the passive acoustic location device directs the robot to the scene where abnormal events occur and the robot can employ its camera to further confirm the occurrence of the events. At last, the robot will send the image captured by the robot to the mobile phone of master.
surveillance robot audio surveillance video surveillance