Statistical accuracy analysis of different detecting algorithms for surveillance system in smart city

Several detecting algorithms are developed for real-time surveillance systems in the smart cities. The most popular algorithms due to its accuracy are: Temporal Differencing, Background Subtraction, and Gaussian Mixture Models. Selecting of which algorithm is the best to be used, based on accuracy, is a good choise, but is not the best. Statistical accuracy anlysis tests are required for achieving a confident decision. This paper presents further analysis of the accuracy by employing four parameters: false recognition, unrecognized, true recognition, and total fragmentation ratios. The results proof that no algorithm is selected as the perfect or suitable for all applications based on the total fragmentation ratio, whereas both false recognition ratio and unrecognized ratio parameters have a significant impact. The mlti-way Analysis of Variate (so-called K-way ANONVA) is used for proofing the results based on SPSS statistics.

• Publication year

  2020

• Venue

  Indonesian Journal of Electrical Engineering and Computer Science

• Publication date

  2020-05-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.11591/ijeecs.v18.i2.pp979-986
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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