Random mapping network for tactical target reacquisition after loss of track

Susan D. Church; Jerry A. Burman

doi:10.1117/12.44878

1 August 1991 Random mapping network for tactical target reacquisition after loss of track

Susan D. Church, Jerry A. Burman

Proceedings Volume 1471, Automatic Object Recognition; (1991) https://doi.org/10.1117/12.44878
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

Prior methods for tactical target reacquisition after a loss of track have used tree classifiers and template matchers. Examples of prior techniques include classifiers that are trained with a priori data which makes them somewhat intolerant to temporal and dynamic changes in the target pattern. Prior methods for reacquisition generally rely on proximity and area-based schemes. The disadvantage of these methods include their dependence on accurate and consistent segmentation in cluttered scenarios and their need for precise target position prediction. The random mapping network algorithm (RMNRA) offers a solution using a pattern memory and sparse feature matching technique. RMNRA assists the imaging tracker and improves tracking tenacity by reacquiring a tracked target after a loss of track has occurred. The reacquisition algorithm uses an associative memory to perform target pattern matching. The pattern matching technique is unique in that it is tolerant to some of the ambiguities that occur with classical template pattern matchers. Weighted pattern feature vectors are stored in a memory matrix to facilitate the matching of sensed and reference patterns dynamically over time. In addition, a sophisticated algorithm was designed to update the memory matrix over time to forget prior patterns as the target signature becomes stale over time and space. The algorithm has been implemented in real-time hardware and flight tested with an infrared sensor. The algorithm is discussed and results using real IR imagery are shown.

Citation Download Citation

Susan D. Church and Jerry A. Burman "Random mapping network for tactical target reacquisition after loss of track", Proc. SPIE 1471, Automatic Object Recognition, (1 August 1991); https://doi.org/10.1117/12.44878

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KEYWORDS

Image segmentation

Detection and tracking algorithms

Associative arrays

Binary data

Logic

Object recognition

Optical correlators

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