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Open Access Publications from the University of California

Exploring Tradeoffs in Accuracy, Energy and Latency of Scale Invariant Feature Transform in Wireless Camera Networks

  • Author(s): Ko, Teresa
  • Charbiwala, Zainul Mohammed
  • Ahmadian, Shaun
  • Rahimi, Mohammed
  • Srivastava, Mani B.
  • Soatto, Stefano
  • Estrin, D
  • et al.

Advances in DSP technology create important avenues of research for embedded vision. One such avenue is the investigation of tradeoffs amongst system parameters which affect the energy, accuracy, and latency of the overall system. This paper reports work on benchmarking the performance and cost of Scale Invariant Feature Transform (SIFT) for visual classification on a Blackfin DSP processor. Through measurements andmodeling of the camera sensor node, we investigate system performance (classification accuracy, latency, energy consumption) in light of image resolution, arithmetic precision, location of processing (local vs. server-side), and processor speed. A case study on counting eggs during avian nesting season is used to experimentally determine the tradeoffs of different design parameters and discuss implications to other application domains.

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