Abstract:
In this paper, we present an approach to Multi-Object Tracking (MOT) that is based on the Ensemble Kalman Filter (EnKF). The EnKF is a standard algorithm for data assimil...Show MoreMetadata
Abstract:
In this paper, we present an approach to Multi-Object Tracking (MOT) that is based on the Ensemble Kalman Filter (EnKF). The EnKF is a standard algorithm for data assimilation in high-dimensional state spaces that is mainly used in geosciences, but has so far only attracted little attention for object tracking problems. In our approach, the Optimal Subpattern Assignment (OSPA) distance is used for coping with unlabeled noisy measurements and a robust covariance estimation is done using FastMCD to deal with possible outliers due to false detections. The algorithm is evaluated and compared against a global nearest neighbour Kalman Filter (NNKF) and a recently proposed JPDA-Ensemble Kalman Filter (JPDA-EnKF) in a simulated scenario with multiple objects and false detections.
Published in: 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Date of Conference: 16-18 November 2017
Date Added to IEEE Xplore: 11 December 2017
ISBN Information: