Systematic acquisition of audio classes for elevator surveillance

Regunathan Radhakrishnan; Ajay Divakaran

doi:10.1117/12.587814

14 March 2005 Systematic acquisition of audio classes for elevator surveillance

Regunathan Radhakrishnan, Ajay Divakaran

Proceedings Volume 5685, Image and Video Communications and Processing 2005; (2005) https://doi.org/10.1117/12.587814
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

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.

Citation Download Citation

Regunathan Radhakrishnan and Ajay Divakaran "Systematic acquisition of audio classes for elevator surveillance", Proc. SPIE 5685, Image and Video Communications and Processing 2005, (14 March 2005); https://doi.org/10.1117/12.587814

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