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Proceedings ArticleDOI

Centroid tracker and aimpoint selection

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TLDR
The real-time hardware implementation of centroid tracker with a suitable thresholding technique is presented including the interfacing to a multimode tracker for autonomous target tracking and aimpoint selection and a microprocessor based subsystem for the system control.
Abstract
Autonomous fire and forget weapons have gained importance to achieve accurate first pass kill by hitting the target at an appropriate aim point. Centroid of the image presented by a target in the field of view (FOV) of a sensor is generally accepted as the aimpoint for these weapons. Centroid trackers are applicable only when the target image is of significant size in the FOV of the sensor but does not overflow the FOV. But as the range between the sensor and the target decreases the image of the target will grow and finally overflow the FOV at close ranges and the centroid point on the target will keep on changing which is not desirable. And also centroid need not be the most desired/vulnerable point on the target. For hardened targets like tanks, proper aimpoint selection and guidance up to almost zero range is essential to achieve maximum kill probability. This paper presents a centroid tracker realization. As centroid offers a stable tracking point, it can be used as a reference to select the proper aimpoint. The centroid and the desired aimpoint are simultaneously tracked to avoid jamming by flares and also to take care of the problems arising due to image overflow. Thresholding of gray level image to binary image is a crucial step in centroid tracker. Different thresholding algorithms are discussed and a suitable algorithm is chosen. The real-time hardware implementation of centroid tracker with a suitable thresholding technique is presented including the interfacing to a multimode tracker for autonomous target tracking and aimpoint selection. The hardware uses very high speed arithmetic and programmable logic devices to meet the speed requirement and a microprocessor based subsystem for the system control. The tracker has been evaluated in a field environment.

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Citations
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Research on tracking approach to low-flying weak small target near the sea - art. no. 60273R

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TL;DR: This method first uses neighborhood averaging method to suppress background and enhance targets so as to increase SNR, and then uses the multi-point multi-layer vertical Sobel operator combined with linear least squares fitting to locate the sea-level line and lastly uses the centroid tracking algorithm to detect and track the target.
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Intelligent video tracking based on fuzzy-reasoning segmentation

TL;DR: Comparative experiments show that the performance of the proposed fuzzy-reasoning segmentation is superior to that of the conventional thresholding methods and the usefulness of the fuzzy- reasoning segmentation for practical applications is demonstrated by considering two sequences of real target images.
References
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Journal ArticleDOI

A Real-Time Video Tracking System

TL;DR: New high-speed algorithms together with fast digital hardware have produced a system for missile and aircraft identification and tracking that possesses a degree of ``intelligence'' not previously implemented in a real-time tracking system.
Proceedings ArticleDOI

Centroid Tracker Aim Point Estimation In The Presence Of Sensor Noise And Clutter

TL;DR: An investigation of the effects of system and sensor noise on target tracking is presented and results obtained are applicable to the clutter noise case when clutter causes pixel classification errors which are random in nature.