Correlation filters due to its three protuberant advantages have proven very effective for automatic target detection, biometric verification and security applications. In this paper, correlation filters are implemented in hardware FPGA keeping in view their importance in real time applications. Hardware implementation results are placed in comparison with results generated through software. These results are almost similar with a negligible variation i.e. 10<sup>-4</sup>, which is demonstrated in the experimental section, in addition to valuable time reduction. The hardware design of these filters is implemented in LabView which can be subsequently employed in real-time security applications. This design may be expanded for other advanced variants of correlation filters in future work.
A fully invariant system helps in resolving difficulties in object detection when camera or object orientation and position
are unknown. In this paper, the proposed correlation filter based mechanism provides the capability to suppress noise,
clutter and occlusion. Minimum Average Correlation Energy (MACE) filter yields sharp correlation peaks while
considering the controlled correlation peak value. Difference of Gaussian (DOG) Wavelet has been added at the
preprocessing stage in proposed filter design that facilitates target detection in orientation variant cluttered environment.
Logarithmic transformation is combined with a DOG composite minimum average correlation energy filter (WMACE),
capable of producing sharp correlation peaks despite any kind of geometric distortion of target object. The proposed
filter has shown improved performance over some of the other variant correlation filters which are discussed in the result
Correlation filters are a well established means for target recognition tasks. However, the unintentional effect of circular
correlation has a negative influence on the performance of correlation filters as they are implemented in frequency
domain. The effects of aliasing are minimized by introducing zero aliasing constraints in the template and test image. In
this paper, the comparative analysis of logarithmic zero aliasing optimal trade off correlation filters has been carried out
for different types of target distortions. The zero aliasing Maximum Average Correlation Height (MACH) filter has been
identified as the best choice based on our research for achieving enhanced results in the presence of any type of variance
which are discussed in results section. The reformulation of the MACH expressions with zero aliasing has been made to
demonstrate the achievable enhancement to the logarithmic MACH filter in target detection applications.
Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.