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Yuan-Kai Wang, Hung-Yu Chen. Intelligent Mobile Video Surveillance System with Multilevel Distillation[J]. Journal of Electronic Science and Technology, 2017, 15(2): 133-140. DOI: 10.11989/JEST.1674-862X.6072011
Citation: Yuan-Kai Wang, Hung-Yu Chen. Intelligent Mobile Video Surveillance System with Multilevel Distillation[J]. Journal of Electronic Science and Technology, 2017, 15(2): 133-140. DOI: 10.11989/JEST.1674-862X.6072011
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Intelligent Mobile Video Surveillance System with Multilevel Distillation

doi: 10.11989/JEST.1674-862X.6072011
  • 1.

    Department of Electrical Engineering,Fu Jen Catholic University,New Taipei City 24205;

  • 2.

    Intelligent System Lab.,Fu Jen Catholic University,New Taipei City 24205

More Information
  • Author Bio:

    Hung-Yu Chen. His research interests include medical electronics, embedded system design, multimedia, and human-machine interface,e-mail:ronwisely@islab.tw

  • Received Date: 2016-07-19
  • Rev Recd Date: 2016-09-14
  • Publish Date: 2017-06-24
    Abstract
  • This paper proposes a mobile video surveillance system consisting of intelligent video analysis and mobile communication networking. This multilevel distillation approach helps mobile users monitor tremendous surveillance videos on demand through video streaming over mobile communication networks. The intelligent video analysis includes moving object detection/tracking and key frame selection which can browse useful video clips. The communication networking services, comprising video transcoding, multimedia messaging, and mobile video streaming, transmit surveillance information into mobile appliances. Moving object detection is achieved by background subtraction and particle filter tracking. Key frame selection, which aims to deliver an alarm to a mobile client using multimedia messaging service accompanied with an extracted clear frame, is reached by devising a weighted importance criterion considering object clarity and face appearance. Besides, a spatial-domain cascaded transcoder is developed to convert the filtered image sequence of detected objects into the mobile video streaming format. Experimental results show that the system can successfully detect all events of moving objects for a complex surveillance scene, choose very appropriate key frames for users, and transcode the images with a high power signal-to-noise ratio (PSNR).
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