  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. Moreover, neural networks are
memory intensive which will take up lots of hardware resources. In this paper,
a feature selection visual tracking algorithm combining CNN based
MDNet(Multi-Domain Network) and RoIAlign was developed. We find that there is a
lot of redundancy in feature maps from convolutional layers. So valid feature
maps are selected by mutual information and others are abandoned which can
reduce the complexity and computation of the network and do not affect the
precision. 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 RoIAlign 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 RoIAlign employed, the computation speed has been increased and it
shows greater precision than RoIPool. Because RoIAlign can process float number
coordinates by bilinear interpolation. These strategies can accelerate the
processing, reduce the complexity with very low impact on precision and it can
run at around 10 fps(while the speed of MDNet is about 1 fps). The proposed
algorithm has been evaluated on a benchmark: OTB100, on which high precision
and speed have been obtained.
