Multiple Target Tracking for Marker-less Augmented Reality

Francis Engelmann, Patrick Sudowe, Bastian Leibe
Master Thesis (January 2014)

In this work, we implemented an AR framework for planar targets based on the ORB feature-point descriptor. The main components of the framework are a detector, a tracker and a graphical overlay. The detector returns a homography that maps the model- image onto the target in the camera-image. The homography is estimated from a set of feature-point correspondences using the Direct Linear Transform (DLT) algorithm and Levenberg-Marquardt (LM) optimization. The outliers in the set of feature-point correspondences are removed using RANSAC. The tracker is based on the Kalman filter, which applies a consistent dynamic movement on the target. In a hierarchical matching scheme, we extract additional matches from consecutive frames and perspectively transformed model-images, which yields more accurate and jitter-free homography estimations. The graphical overlay computes the six-degree-of-freedom (6DoF) pose from the estimated homography. Finally, to visualize the computed pose, we draw a cube on the surface of the tracked target. In the evaluation part, we analyze the performance of our system by looking at the accuracy of the estimated homography and the ratio of correctly tracked frames. The evaluation is based on the ground truth provided by two datasets. We evaluate most components of the framework under different target movements and lighting conditions. In particular, we proof that our framework is robust against considerable perspective distortion and show the benefit of using the hierarchical matching scheme to minimize jitter and improve accuracy.

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