Extended Target Tracking and Classification Using Neural Networks

TUNCER B., KUMRU M., ÖZKAN E.

22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, 2 - 05 July 2019, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Ottawa
  • Country: Canada
  • Keywords: Extended Target Tracking, Contour Representation, Shape-based Classification, Gaussian Process, Artificial Neural Network, Classification, Deep Learning, JOINT TRACKING, RECOGNITION, OBJECT, SCALE, RADAR
  • Middle East Technical University Affiliated: Yes

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.