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摘要
We propose a novel visual tracking algorithm based on the representations from a pre-trained Convolutional Neural Network (CNN). Our algorithm pre-trains a simplified CNN using a large set of videos with tracking ground truths to obtain a generic target representation. When tracking, Particle Filtering (PF) is combined to the fully-connected layer in the pre-trained CNN. Deep representations and hand-crafted features help to model tracking. To optimize the particles' distribution, the velocity and acceleration information aids to calculate dynamic model. Meanwhile, our algorithm updates the tracking model in a lazy manner to avoid shift and expensive computation. As compared to previous methods, our results demonstrate superior performances in existing tracking benchmarks.
