  The deep learning-based visual tracking algorithms such as MDNet achieve high
performance leveraging to the feature extraction ability of a deep neural
network. However, the tracking efficiency of these trackers is not very high
due to the slow feature extraction for each frame in a video. In this paper, we
propose an effective tracking algorithm to alleviate the time-consuming
problem. Specifically, we design a deep flow collaborative network, which
executes the expensive feature network only on sparse keyframes and transfers
the feature maps to other frames via optical flow. Moreover, we raise an
effective adaptive keyframe scheduling mechanism to select the most appropriate
keyframe. We evaluate the proposed approach on large-scale datasets: OTB2013
and OTB2015. The experiment results show that our algorithm achieves
considerable speedup and high precision as well.
