  Correlation filter (CF)-based methods have demonstrated exceptional
performance in visual object tracking for unmanned aerial vehicle (UAV)
applications, but suffer from the undesirable boundary effect. To solve this
issue, spatially regularized correlation filters (SRDCF) proposes the spatial
regularization to penalize filter coefficients, thereby significantly improving
the tracking performance. However, the temporal information hidden in the
response maps is not considered in SRDCF, which limits the discriminative power
and the robustness for accurate tracking. This work proposes a novel approach
with dynamic consistency pursued correlation filters, i.e., the CPCF tracker.
Specifically, through a correlation operation between adjacent response maps, a
practical consistency map is generated to represent the consistency level
across frames. By minimizing the difference between the practical and the
scheduled ideal consistency map, the consistency level is constrained to
maintain temporal smoothness, and rich temporal information contained in
response maps is introduced. Besides, a dynamic constraint strategy is proposed
to further improve the adaptability of the proposed tracker in complex
situations. Comprehensive experiments are conducted on three challenging UAV
benchmarks, i.e., UAV123@10FPS, UAVDT, and DTB70. Based on the experimental
results, the proposed tracker favorably surpasses the other 25 state-of-the-art
trackers with real-time running speed ($\sim$43FPS) on a single CPU.

Computer Vision and Pattern Recognition