• DocumentCode
    1194067
  • Title

    Efficient Content Analysis Engine for Visual Surveillance Network

  • Author

    Chan, Wei-Kai ; Chang, Jing-Ying ; Chen, Tse-Wei ; Tseng, Yu-Hsiang ; Chien, Shao-Yi

  • Author_Institution
    Grad. Inst. of Electron. Eng., Nat. Taiwan Univ., Taipei
  • Volume
    19
  • Issue
    5
  • fYear
    2009
  • fDate
    5/1/2009 12:00:00 AM
  • Firstpage
    693
  • Lastpage
    703
  • Abstract
    In the next-generation visual surveillance systems, content analysis tools will be integrated. In this paper, to accelerate these tools, it is proposed to integrate a hardware content analysis engine into a smart camera system-on-a-chip (SoC). A smart camera SoC hardware architecture with the proposed visual content analysis engine is first presented. This engine consists of dedicated accelerators and a programmable morphology coprocessor. Stream processing design concept, frame-level pipelining, and subword level parallelism are employed together to efficiently utilize the bandwidth of the system bus and achieve high throughput. The implementation results show that, with 168 K logic gates and 40.63 Kb on-chip memory, a processing speed of 30 640 x 480 frames/s can be achieved, while the operations of video object segmentation, object description and tracking, and face detection and scoring are supported.
  • Keywords
    cameras; coprocessors; logic gates; surveillance; system-on-chip; face detection; frame-level pipelining; hardware content analysis engine; logic gates; object description; object tracking; on-chip memory; programmable morphology coprocessor; smart camera; stream processing design concept; subword level parallelism; system-on-a-chip; video object segmentation; visual surveillance network; Content analysis engine; smart camera; surveillance; system-on-a-chip;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems for Video Technology, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1051-8215
  • Type

    jour

  • DOI
    10.1109/TCSVT.2009.2017408
  • Filename
    4801624