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Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated.
DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from (x, y).
In the last decade many different algorithms have been proposed to track a generic object in videos.
Human following on mobile robots has witnessed significant advances due to its potentials for real-world applications.
The trajectory and boundary of an orbiting satellite are fundamental information for on-orbit repairing and manipulation by space robots.
Despite the fact that tremendous advances have been made by numerous recent tracking approaches in the last decade, how to achieve high-performance visual tracking is still an open problem.
We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter.
Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected.