  Deep neural networks, albeit their great success on feature learning in
various computer vision tasks, are usually considered as impractical for online
visual tracking because they require very long training time and a large number
of training samples. In this work, we present an efficient and very robust
tracking algorithm using a single Convolutional Neural Network (CNN) for
learning effective feature representations of the target object, in a purely
online manner. Our contributions are multifold: First, we introduce a novel
truncated structural loss function that maintains as many training samples as
possible and reduces the risk of tracking error accumulation. Second, we
enhance the ordinary Stochastic Gradient Descent approach in CNN training with
a robust sample selection mechanism. The sampling mechanism randomly generates
positive and negative samples from different temporal distributions, which are
generated by taking the temporal relations and label noise into account.
Finally, a lazy yet effective updating scheme is designed for CNN training.
Equipped with this novel updating algorithm, the CNN model is robust to some
long-existing difficulties in visual tracking such as occlusion or incorrect
detections, without loss of the effective adaption for significant appearance
changes. In the experiment, our CNN tracker outperforms all compared
state-of-the-art methods on two recently proposed benchmarks which in total
involve over 60 video sequences. The remarkable performance improvement over
the existing trackers illustrates the superiority of the feature
representations which are learned
