Abstract:

We present a systematic framework for arriving at audio classes for detection of crimes in elevators. We use our time series analysis framework proposed in5 to low-level features extracted from the audio of an elevator surveillance content to perform an inlier/outlier based temporal segmentation. Since suspicious events in elevators are outliers in a background of usual events, such a segmentation help bring out such events without any a priori knowledge. Then, by performing an automatic clustering on the detected outliers, we identify consistent patterns for which we can train supervised detectors. We apply the proposed framework to a colleciton of elevator surveillance audio data to systematically acquire audio classes such as banging, footsteps, non-neutral speech and normal speech etc. Based on the observation that the banging audio class and non-neutral speech class are indicative of suspicous events in the elevator data set, we are able to detect all of the suspicious activities without any misses.