基于校正神经网络的视频追踪算法

Visual tracking algorithm based on verifying networks

针对现有基于深度学习的视频追踪算法关注深层特征而忽略浅层特征以及追踪网络没有对每帧追踪结果进行检测的问题,提出基于校正神经网络的视频追踪算法。该算法包含追踪网络和校正网络。在追踪网络中,考虑到深层特征和浅层边缘特征的融合,设计一个多输入的残差网络,学习目标和对应的高斯响应图之间的关系,从而获得目标对象的位置信息。在校正网络中,设计浅层链式判别网络,将两个网络的追踪结果进行比较,根据比较结果对追踪网络进行在线更新。本算法既考虑了深层特征,又避免了细节信息的丢失;同时,对追踪结果进行评判,防止网络更新中延续错误信息。对比试验说明本研究所提的追踪算法比现有的一些追踪方法取得更好的追踪结果。

In order to solve the problem that the existing deep learning based visual tracking algorithms paid attention to the deep features but neglected the shallow features, and the tracking network did not evaluate the tracking results, a visual tracking algorithm based on verifying network was proposed. The proposed algorithm consisted of tracking network and verifying network. In the tracking network, considering the fusion of deep features and shallow edge features, a multi-input residual network was designed to learn the relationship between the target and its corresponding Gaussian response map to obtain the position information of the target. In the verifying network, a shallow chain discriminate network was designed, and this paper compared the tracking results of tracking network and verifying network, and updated the tracking network according to the compared results. Therefore, the proposed algorithm not only took the deep features into account, but also avoided the loss of detail information. Furthermore, the tracking results were evaluated to prevent the continuation of error messages in the update. The experimental results illustrated that the proposed tracking algorithm achieved better tracking results than some other existing tracking methods.