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http://dx.doi.org/10.12673/jant.2022.26.6.522

Object Tracking Using Adaptive Scale Factor Neural Network  

Sun-Bae Park (Department of Electronic and Electrical Engineering, Graduate School, Hongik University)
Do-Sik Yoo (Department of Electronic and Electrical Engineering, Graduate School, Hongik University)
Publication Information
Journal of Advanced Navigation Technology / v.26, no.6, 2022 , pp. 522-527 More about this Journal
Abstract
Object tracking is a field of signal processing that sequentially tracks the location of an object based on the previous-time location estimations and the present-time observation data. In this paper, we propose an adaptive scaling neural network that can track and adjust the scale of the input data with three recursive neural network (RNN) submodules. To evaluate object tracking performance, we compare the proposed system with the Kalman filter and the maximum likelihood object tracking scheme under an one-dimensional object movement model in which the object moves with piecewise constant acceleration. We show that the proposed scheme is generally better, in terms of root mean square error (RMSE) performance, than maximum likelihood scheme and Kalman filter and that the performance gaps grow with increased observation noise.
Keywords
Artificial neural network; Kalman filter; Maximum likelihood method; Object tracking; Recurrent neural network;
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Times Cited By KSCI : 1  (Citation Analysis)
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