Improving Model Drift for Robust Object Tracking
This work addresses model drift for robust object tracking in complex scenes, representing an incremental improvement over existing correlation filter-based trackers.
The paper tackles model drift in object tracking by proposing a method to detect primary and secondary peaks in response maps and using an adaptive update mechanism, achieving superior performance on OTB2013, OTB100, and TC128 datasets with real-time speed.
Discriminative correlation filters show excellent performance in object tracking. However, in complex scenes, the apparent characteristics of the tracked target are variable, which makes it easy to pollute the model and cause the model drift. In this paper, considering that the secondary peak has a greater impact on the model update, we propose a method for detecting the primary and secondary peaks of the response map. Secondly, a novel confidence function which uses the adaptive update discriminant mechanism is proposed, which yield good robustness. Thirdly, we propose a robust tracker with correlation filters, which uses hand-crafted features and can improve model drift in complex scenes. Finally, in order to cope with the current trackers' multi-feature response merge, we propose a simple exponential adaptive merge approach. Extensive experiments are performed on OTB2013, OTB100 and TC128 datasets. Our approach performs superiorly against several state-of-the-art trackers while runs at speed in real time.