This paper introduces a novel deep learning based approach for vision based
single target tracking. We address this problem by proposing a network
architecture which takes the input video frames and directly computes the
tracking score for any candidate target location by estimating the probability
distributions of the positive and negative examples. This is achieved by
combining a deep convolutional neural network with a Bayesian loss layer in a
unified framework. In order to deal with the limited number of positive
training examples, the network is pre-trained offline for a generic image
feature representation and then is fine-tuned in multiple steps. An online
fine-tuning step is carried out at every frame to learn the appearance of the
target. We adopt a two-stage iterative algorithm to adaptively update the
network parameters and maintain a probability density for target/non-target
regions. The tracker has been tested on the standard tracking benchmark and the
results indicate that the proposed solution achieves state-of-the-art tracking
results.

本文提出了一种基于深度学习的新方法，用于基于视觉的单目标跟踪，该方法结合了卷积神经网络和贝叶斯损失层，提出了一种直接计算候选目标位置跟踪得分的网络架构。在标准跟踪基准上，该跟踪器达到了最先进的跟踪结果。