  Visual tracking addresses the problem of identifying and localizing an
unknown target in a video given the target specified by a bounding box in the
first frame. In this paper, we propose a dual network to better utilize
features among layers for visual tracking. It is observed that features in
higher layers encode semantic context while its counterparts in lower layers
are sensitive to discriminative appearance. Thus we exploit the hierarchical
features in different layers of a deep model and design a dual structure to
obtain better feature representation from various streams, which is rarely
investigated in previous work. To highlight geometric contours of the target,
we integrate the hierarchical feature maps with an edge detector as the coarse
prior maps to further embed local details around the target. To leverage the
robustness of our dual network, we train it with random patches measuring the
similarities between the network activation and target appearance, which serves
as a regularization to enforce the dual network to focus on target object. The
proposed dual network is updated online in a unique manner based on the
observation that the target being tracked in consecutive frames should share
more similar feature representations than those in the surrounding background.
It is also found that for a target object, the prior maps can help further
enhance performance by passing message into the output maps of the dual
network. Therefore, an independent component analysis with reference algorithm
(ICA-R) is employed to extract target context using prior maps as guidance.
Online tracking is conducted by maximizing the posterior estimate on the final
maps with stochastic and periodic update. Quantitative and qualitative
evaluations on two large-scale benchmark data sets show that the proposed
algorithm performs favourably against the state-of-the-arts.
