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Proceedings Paper

Systematic acquisition of audio classes for elevator surveillance
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Paper Abstract

We present a systematic framework for arriving at audio classes for detection of crimes in elevators. We use a time series analysis framework to analyze the 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 collection 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 suspicious events in the elevator data set, we are able to detect all of the suspicious activities without any misses.

Paper Details

Date Published: 14 March 2005
PDF: 8 pages
Proc. SPIE 5685, Image and Video Communications and Processing 2005, (14 March 2005); doi: 10.1117/12.587814
Show Author Affiliations
Regunathan Radhakrishnan, Mitsubishi Electric Research Labs. (United States)
Ajay Divakaran, Mitsubishi Electric Research Labs. (United States)


Published in SPIE Proceedings Vol. 5685:
Image and Video Communications and Processing 2005
Amir Said; John G. Apostolopoulos, Editor(s)

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