Optical joint transform correlation (JTC) has been proven to be an efficient pattern recognition tool,
especially, for real-time applications. However, the classical JTC suffers from a lot of limitations such as broad correlation peaks, large side lobes, duplicate correlation peaks and low discrimination
between target and non-target objects. This paper proposes a nonlinear JTC based target detection and tracking technique, where the reference image is phase-shifted and phase-encoded and then fed
to two parallel processing channels. Each channel introduces the unknown input scene and performs Fourier transformations to obtain the joint power spectra signals, which are then combined and
phase-encoded. Then a nonlinear operation is performed on the modified power spectrum followed by the application of fringe-adjusted filtering operation. A subsequent inverse Fourier transform
operation yields the correlation output containing a highly distinct peak corresponding to each target present in the input scene. The reference image phase-encoding process removes any overlapping
issue among the input scene objects, which is a drawback of classical JTC technique. An updated
decision criterion is developed for the correlation plane so that it can accurately identify the location
of the target. The proposed pattern recognition technique offers an excellent alternative for target
tracking in an unknown video sequence.
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