  Most of the existing tracking methods based on CNN(convolutional neural
networks) are too slow for real-time application despite the excellent tracking
precision compared with the traditional ones. In this paper, a fast dynamic
visual tracking algorithm combining CNN based MDNet(Multi-Domain Network) and
RoIAlign was developed. The major problem of MDNet also lies in the time
efficiency. Considering the computational complexity of MDNet is mainly caused
by the large amount of convolution operations and fine-tuning of the network
during tracking, a RoIPool layer which could conduct the convolution over the
whole image instead of each RoI is added to accelerate the convolution and a
new strategy of fine-tuning the fully-connected layers is used to accelerate
the update. With RoIPool employed, the computation speed has been increased but
the tracking precision has dropped simultaneously. RoIPool could lose some
positioning precision because it can not handle locations represented by
floating numbers. So RoIAlign, instead of RoIPool, which can process floating
numbers of locations by bilinear interpolation has been added to the network.
The results show the target localization precision has been improved and it
hardly increases the computational cost. These strategies can accelerate the
processing and make it 7x faster than MDNet with very low impact on precision
and it can run at around 7 fps. The proposed algorithm has been evaluated on
two benchmarks: OTB100 and VOT2016, on which high precision and speed have been
obtained. The influence of the network structure and training data are also
discussed with experiments.
