Subpixel-Precise Tracking of Rigid Objects in Real-time
This work addresses the problem of precise and fast object tracking for applications like robotics or surveillance, though it appears incremental as it builds on existing real-time trackers.
The paper tackles real-time rigid object tracking by using subpixel-precise image edges, achieving high accuracy in position, scale, and rotation at around 80fps with robustness to occlusion and illumination changes.
We present a novel object tracking scheme that can track rigid objects in real time. The approach uses subpixel-precise image edges to track objects with high accuracy. It can determine the object position, scale, and rotation with subpixel-precision at around 80fps. The tracker returns a reliable score for each frame and is capable of self diagnosing a tracking failure. Furthermore, the choice of the similarity measure makes the approach inherently robust against occlusion, clutter, and nonlinear illumination changes. We evaluate the method on sequences from rigid objects from the OTB-2015 and VOT2016 dataset and discuss its performance. The evaluation shows that the tracker is more accurate than state-of-the-art real-time trackers while being equally robust.