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27 March 1997 Maximum-likelihood approach to target tracking on image sequences
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Abstract
Until now, most optical pattern recognition filters have been designed to process one image at a time. In contrast, many point-source target processing algorithms utilize successive frame integration to enhance the signal-to- clutter ratio. Our aim is to utilize the temporal correlation between successive frames in order to improve the tracking of extended targets appearing on very cluttered backgrounds. In our image model, the successive frames are assumed to consist of a moving object appearing on a moving background. From this model, the maximum-likelihood processor for tracking the object from one frame to the next one is derived. Given some simplifying assumptions, this processor is shown to consist in the linear combination of two sub-processors which are based on correlation operation. They could thus be implemented on a hybrid optoelectronical system that utilizes the rapidity of optical correlation.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Francois Goudail and Philippe Refregier "Maximum-likelihood approach to target tracking on image sequences", Proc. SPIE 3073, Optical Pattern Recognition VIII, (27 March 1997); https://doi.org/10.1117/12.270383
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