  We propose a novel visual tracking algorithm based on the representations
from a discriminatively trained Convolutional Neural Network (CNN). Our
algorithm pretrains a CNN using a large set of videos with tracking
ground-truths to obtain a generic target representation. Our network is
composed of shared layers and multiple branches of domain-specific layers,
where domains correspond to individual training sequences and each branch is
responsible for binary classification to identify the target in each domain. We
train the network with respect to each domain iteratively to obtain generic
target representations in the shared layers. When tracking a target in a new
sequence, we construct a new network by combining the shared layers in the
pretrained CNN with a new binary classification layer, which is updated online.
Online tracking is performed by evaluating the candidate windows randomly
sampled around the previous target state. The proposed algorithm illustrates
outstanding performance compared with state-of-the-art methods in existing
tracking benchmarks.
