  Visual tracking is intrinsically a temporal problem. Discriminative
Correlation Filters (DCF) have demonstrated excellent performance for
high-speed generic visual object tracking. Built upon their seminal work, there
has been a plethora of recent improvements relying on convolutional neural
network (CNN) pretrained on ImageNet as a feature extractor for visual
tracking. However, most of their works relying on ad hoc analysis to design the
weights for different layers either using boosting or hedging techniques as an
ensemble tracker. In this paper, we go beyond the conventional DCF framework
and propose a Kernalised Multi-resolution Convnet (KMC) formulation that
utilises hierarchical response maps to directly output the target movement.
When directly deployed the learnt network to predict the unseen challenging UAV
tracking dataset without any weight adjustment, the proposed model consistently
achieves excellent tracking performance. Moreover, the transfered
multi-reslution CNN renders it possible to be integrated into the RNN temporal
learning framework, therefore opening the door on the end-to-end temporal deep
learning (TDL) for visual tracking.
