A Visual Tracking Method Based on an Adaptive Overlapping Correlation Filter for Robotic Real-Time Cognitive Imaging

Abstract EN

Computer vision is a very important research direction in the cognitive computing field.

Robots encounter various target-tracking problems with computer vision systems.

Robust scale estimation is an important issue in tracking algorithms.

Most of the available methods have difficulty addressing even reasonable changes of scale in complex videos.

In this paper, we propose a visual tracking method based on robust scale estimation, which uses a discriminant correlation filter based on a time-dependent scale-space filter and an adaptive cross-correlation filter.

The tracker uses separate essential filters for sample migration and scale estimation.

Furthermore, the built-in scale estimation method can be introduced into other tracking algorithms.

We validate the proposed method on the UAV123 dataset.

The results of comparison experiments with the traditional correlation filter tracking method demonstrate that the proposed method improves the success rate and tracking accuracy while controlling the computational complexity; its success rate measured by the area under the curve is 0.638, while at a location error precision of 20%, it is 0.649.