Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

Abstract

With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevertheless, the inherent issues of boundary effects and filter degradation, as well as occlusion and background clutter, degrade the tracking performance. In this work, we proposed an augmented memory joint aberrance repressed correlation filter (AMRCF) for visual tracking. Based on the background-aware correlation filter (BACF), we introduced adaptive spatial regularity to mitigate the boundary effect. Several historical views and the current view are exploited to train the model together as a way to reinforce the memory. Furthermore, aberrance repression regularization was introduced to suppress response anomalies due to occlusion and deformation, while adopting the dynamic updating strategy to reduce the impact of anomalies on the appearance model. Finally, extensive experimental results over four well-known tracking benchmarks indicate that the proposed AMRCF tracker achieved comparable tracking performance to most state-of-the-art (SOTA) trackers.

Learning Background-Suppressed Dual-Regression Correlation Filters for Visual Tracking

“…However, such passive learning strategies may not be effective, as they do not prevent the occurrence of response aberrations. To achieve background suppression, some methods [17][18][19][20] use the known previous frame's response as a template to limit the variation rate of the current frame's response, thus effectively limiting drastic response changes. Some trackers [6,17,21,22] suppress the background in a very straightforward way, i.e., using a spatial constraint matrix to mask or ignore the background region.…”