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

• A novel technique for detecting changes in low cost MWIR cameras is proposed.
• We propose the design of a preprocessing stage to eliminate noise based on empirical mode decomposition.
• The next stage is the estimate of the drift, which is efficiently achieved by just applying the Kalman filtering.
• The results demonstrate that the proposed method provides very good results for target detection.
• The proposed technique is also compared with the discrete wavelet transform.

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.