Target detection for low cost uncooled MWIR cameras based on empirical mode decomposition Articles uri icon

publication date

  • March 2014

start page

  • 222

end page

  • 231

issue

  • MAR

volume

  • 63

international standard serial number (ISSN)

  • 1350-4495

electronic international standard serial number (EISSN)

  • 1879-0275

abstract

  • In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.

keywords

  • change detection; background subtraction; empirical mode decomposition (emd) intrinsic mode function (imf); drift; denoising