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Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basica lly, if a picture saysa thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.  Keywords:autonomous tracking, feature extraction, feature detector, neural network, target recognition, data reduction.   1.,INTRODUCTION  Automatic target tracking technology has a wide variety of applications. Tracking technology has been researched for use with navigation of Martian landers, rovers, or on Earth for avionics testing or various military applications.  Domestic applications abound and a successful technology which is capable of robust application to a variety of scenarios with little expert technical attention is still under investigation. Specific applications often have solutions where a peculiar feature may be available, but that feature is not expected applicable to the general tracking problem. In order to obtain a general purpose tracking tool, a neural network method was employed. The goal of such a tool was to simplify the necessary inputs given by the operator; thus, a general robust, yet simple method was sought.

Neural network tracking and extension of positive tracking periods