Extended Target Tracking and Classification Using Neural Networks

Date

2019-01-01

Author

Tuncer, Barkın
Kumru, Murat
Özkan, Emre

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Extended Target Tracking, Contour Representation, Shape-based Classification, Gaussian process, Artificial neural network, Classification, Deep Learning

URI

https://hdl.handle.net/11511/53529

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

B. Tuncer, M. Kumru, and E. Özkan, “Extended Target Tracking and Classification Using Neural Networks,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53529.