  Visual object tracking remains an active research field in computer vision
due to persisting challenges with various problem-specific factors in
real-world scenes. Many existing tracking methods based on discriminative
correlation filters (DCFs) employ feature extraction networks (FENs) to model
the target appearance during the learning process. However, using deep feature
maps extracted from FENs based on different residual neural networks (ResNets)
has not previously been investigated. This paper aims to evaluate the
performance of twelve state-of-the-art ResNet-based FENs in a DCF-based
framework to determine the best for visual tracking purposes. First, it ranks
their best feature maps and explores the generalized adoption of the best
ResNet-based FEN into another DCF-based method. Then, the proposed method
extracts deep semantic information from a fully convolutional FEN and fuses it
with the best ResNet-based feature maps to strengthen the target representation
in the learning process of continuous convolution filters. Finally, it
introduces a new and efficient semantic weighting method (using semantic
segmentation feature maps on each video frame) to reduce the drift problem.
Extensive experimental results on the well-known OTB-2013, OTB-2015, TC-128 and
VOT-2018 visual tracking datasets demonstrate that the proposed method
effectively outperforms state-of-the-art methods in terms of precision and
robustness of visual tracking.
