TR2005-076

Systematic Acquisition of Audio Classes for Elevator Surveillance


    •  Radhakrishnan, R., Divakaran, A., "Systematic Acquisition of Audio Classes for Elevator Surveillance", SPIE Conference on Image and Video Communications and Processing, March 2005, vol. 5685, pp. 64-71.
      BibTeX TR2005-076 PDF
      • @inproceedings{Radhakrishnan2005mar,
      • author = {Radhakrishnan, R. and Divakaran, A.},
      • title = {Systematic Acquisition of Audio Classes for Elevator Surveillance},
      • booktitle = {SPIE Conference on Image and Video Communications and Processing},
      • year = 2005,
      • volume = 5685,
      • pages = {64--71},
      • month = mar,
      • url = {https://www.merl.com/publications/TR2005-076}
      • }
  • Research Areas:

    Artificial Intelligence, Speech & Audio

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.

 

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