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A space variant Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter is designed specifically for images acquired from a forward looking infrared (FLIR) sensor, using the maximum of the power spectral density (PSD) of the input image instead of the white noise covariance factor. The kernel can be locally modified depending upon its position in the input frame, which enables adaptation of the filter dependant on background heat signature variances and also enables the normalization of the filter energy levels. The detection capabilities of the filter were evaluated using different data sets of real images and 3D models for a suspected threat in order to define a thresholding parameter. The parameter was based on peak to correlation energy (PCE) and peak to side lobe ratio (PSR) of the correlation output which led to the definition of a criterion for predicting true and false detections. The hardware implementation of the system has been discussed in terms of FPGA versus DSP chipsets and a performance benchmark has been created using millions of multiply-accumulate operations per second (MMAC) and the cost. In this paper we propose an implementation and performance evaluation of a security detection application which uses a space variant OT-MACH filter with different data sets. Also a performance benchmark has been created for the hardware implementation of the proposed system on popular FPGA and DSP chipsets.