  In recent years, deep learning based visual tracking methods have obtained
great success owing to the powerful feature representation ability of
Convolutional Neural Networks (CNNs). Among these methods, classification-based
tracking methods exhibit excellent performance while their speeds are heavily
limited by the expensive computation for massive proposal feature extraction.
In contrast, matching-based tracking methods (such as Siamese networks) possess
remarkable speed superiority. However, the absence of online updating renders
these methods unadaptable to significant object appearance variations. In this
paper, we propose a novel real-time visual tracking method, which adopts an
object-adaptive LSTM network to effectively capture the video sequential
dependencies and adaptively learn the object appearance variations. For high
computational efficiency, we also present a fast proposal selection strategy,
which utilizes the matching-based tracking method to pre-estimate dense
proposals and selects high-quality ones to feed to the LSTM network for
classification. This strategy efficiently filters out some irrelevant proposals
and avoids the redundant computation for feature extraction, which enables our
method to operate faster than conventional classification-based tracking
methods. In addition, to handle the problems of sample inadequacy and class
imbalance during online tracking, we adopt a data augmentation technique based
on the Generative Adversarial Network (GAN) to facilitate the training of the
LSTM network. Extensive experiments on four visual tracking benchmarks
demonstrate the state-of-the-art performance of our method in terms of both
tracking accuracy and speed, which exhibits great potentials of recurrent
structures for visual tracking.
