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Visual Motion Tracking for Estimation of Kite Dynamics


M. Polzin, H. Hesse, T. A. Wood, R. S. Smith

Airborne Wind Energy Conference (AWEC), Delft, Netherlands

This work presents a novel estimation approach for an autonomous tethered kite system. We propose an estimator which relies on visual motion tracking of the kite position from ground-based video recording. The proposed visual tracking approach is used to assess the quality of existing estimators and is eventually applied for real-time estimation of the kite dynamics in closed-loop operation of the AWE system developed at Fachhochschule Nordwestschweiz (FHNW). The focus in this work is on the development of a fast and reliable visual tracking algorithm that can be used for real-time estimation in experiments of autonomous tethered kites.

The developed visual tracking algorithm combines a fast tracking algorithm with a reliable object detector to exploit advantages of both methods. The implemented tracker is a dual correlation filter (DCF), first proposed in [1]. It is not suitable for long-term robust motion tracking since erroneous tracking occurs occasionally. These inevitable tracking failures can be tackled by resetting the tracking algorithm with assistance of a detector [2]. The latter is much more discriminating but comes with a significant computational cost. A reliability measure was therefore added to the DCF tracking algorithm in this work to provide a threshold when the detector is triggered. This improves computational efficiency of the combined visual tracking algorithm and allows reliable real-time estimation of the kite dynamics.

To demonstrate the proposed estimator, we have implemented the visual tracking algorithm in Matlab which is able to track objects (40x40 pixel) in over 100 frames (1280x960 pixel) per second. For three videos of representative kite power test scenarios (sunny, cloudy and a small kite on long lines) with 23300 frames each, we achieve accurate estimates of the kite state. Figure 1 highlights the effect of line dynamics in state estimation from experimental data of the two-line system at FHNW. The markers clearly demonstrate the lag introduced in line-angle-based estimates which is especially evident in (up-loop) curves where line tension is low.

ETH Visual Motion Tracking of Kites
Fig. 1 Kite path based on filtered line angle measurements against visual motion tracking. Markers illustrate the instantaneous tracked positions at two different time instances.

[1] Henriques J., Caseiro R., Martins P., Batista J.: High-Speed Tracking with Kernelized Correlation Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 583-596 (2015).
[2] Dollar P., Appel R., Belongie S., Perona P.: Fast Feature Pyramids for Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(8), 1532-1545 (2014).

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