时间一致性保持的多任务稀疏深度表达视觉跟踪

Temporal Consistency Preserving Multi-Mask Sparse Deep Representation for Visual Tracking

建立一个既能充分考虑目标表观表达的判别性、又能在后续的跟踪过程中保持特征的时间一致性的模型,是解决跟踪问题的关键。为了提高跟踪算法的特征表达判别性和解决跟踪过程中的特征时效性退化问题,文中提出了一种时间一致性保持的稀疏深度表达的跟踪方法。首先,利用不同卷积层上的特征有不同的属性来构建多任务的稀疏深度表达学习方法,充分挖掘多源信息的相关性。其次,利用相关帧的残差构建时间一致性约束正则项,以对跟踪过程特征的退化起到补偿作用,提高了跟踪算法特征的时间一致性。大量实验视频的跟踪结果显示,相比当前的主流算法,所提算法在复杂背景、快速运动等情况下具有更好的跟踪效果和稳定性。

Building a model that can not only fully consider the discriminability of the object appearance,but also keep the temporal consistency of the features in tracking process is the key to solve the tracking problem.In order to improve the discrimination of feature representation and alleviate the degradation of feature in tracking process,a novel temporal consistency preserving multi-mask sparse deep representation method for visual tracking is proposed in the paper.Firstly,multi-task sparse deep expression learning method is constructed by using different feature attributes of deep convolution features on different layers to fully explore the correlation of multi-source information.Secondly,the temporal consistency constrained regularization term is constructed by the residual of relevant frames,which can compensate for the degradation of tracking process features and improve the temporal consistency of tracking features.Numerous experimental results on Benchmark show that this algorithm has better tracking effectiveness and stability than the current state-of-the-art methods in complex background,fast motion and other situations.