Extended Target Tracking and Classification Using Neural Networks | IEEE Conference Publication | IEEE Xplore

Extended Target Tracking and Classification Using Neural Networks


Abstract:

Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT alg...Show More

Abstract:

Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT algorithms can efficiently exploit the available information in these measurements such that they can track the dynamic behaviour of objects and learn their shapes simultaneously. Once the shape estimate of an object is formed, it can naturally be utilized by high-level tasks such as classification of the object type. In this work, we propose to use a naively deep neural network, which consists of one input, two hidden and one output layers, to classify dynamic objects regarding their shape estimates. The proposed method shows superior performance in comparison to a Bayesian classifier for simulation experiments.
Date of Conference: 02-05 July 2019
Date Added to IEEE Xplore: 27 February 2020
ISBN Information:
Conference Location: Ottawa, ON, Canada

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