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Probabilistic tracking algorithms typically rely on graphical models based on the first-order Markov assumption. Although such linear structure models are simple and reasonable, it is not appropriate for persistent tracking since temporal failures by short-term occlusion, shot changes, and appearanc ...

We propose an online visual tracking algorithm by learning discriminative saliency map using Convolutional Neural Network (CNN). Given a CNN pre-trained on a large-scale image repository in offline, our algorithm takes outputs from hidden layers of the network as feature descriptors since they show ...