  Humans can easily learn new concepts from just a single exemplar, mainly due
to their remarkable ability to imagine or hallucinate what the unseen exemplar
may look like in different settings. Incorporating such an ability to
hallucinate diverse new samples of the tracked instance can help the trackers
alleviate the over-fitting problem in the low-data tracking regime. To achieve
this, we propose an effective adversarial approach, denoted as adversarial
"hallucinator" (AH), for robust visual tracking. The proposed AH is designed to
firstly learn transferable non-linear deformations between a pair of
same-identity instances, and then apply these deformations to an unseen tracked
instance in order to generate diverse positive training samples. By
incorporating AH into an online tracking-by-detection framework, we propose the
hallucinated adversarial tracker (HAT), which jointly optimizes AH with an
online classifier (e.g., MDNet) in an end-to-end manner. In addition, a novel
selective deformation transfer (SDT) method is presented to better select the
deformations which are more suitable for transfer. Extensive experiments on 3
popular benchmarks demonstrate that our HAT achieves the state-of-the-art
performance.
