  Single visual object tracking from an unmanned aerial vehicle (UAV) poses
fundamental challenges such as object occlusion, small-scale objects,
background clutter, and abrupt camera motion. To tackle these difficulties, we
propose to integrate the 3D structure of the observed scene into a
detection-by-tracking algorithm. We introduce a pipeline that combines a
model-free visual object tracker, a sparse 3D reconstruction, and a state
estimator. The 3D reconstruction of the scene is computed with an image-based
Structure-from-Motion (SfM) component that enables us to leverage a state
estimator in the corresponding 3D scene during tracking. By representing the
position of the target in 3D space rather than in image space, we stabilize the
tracking during ego-motion and improve the handling of occlusions, background
clutter, and small-scale objects. We evaluated our approach on prototypical
image sequences, captured from a UAV with low-altitude oblique views. For this
purpose, we adapted an existing dataset for visual object tracking and
reconstructed the observed scene in 3D. The experimental results demonstrate
that the proposed approach outperforms methods using plain visual cues as well
as approaches leveraging image-space-based state estimations. We believe that
our approach can be beneficial for traffic monitoring, video surveillance, and
navigation.
