Gao Junyu, Zhang Tianzhu, Yang Xiaoshan, Xu Changsheng
IEEE Trans Image Process. 2018 Mar 9. doi: 10.1109/TIP.2018.2813166.
Most existing part based tracking methods are part-to-part trackers, which usually have two separated steps including part matching and target localization. Different from existing methods, in this paper, we propose a novel part-totarget (P2T) tracker in a unified fashion by inferring target location from parts directly. To achieve this goal, we propose a novel deep regression model for part to target regression in an end-to-end framework via Convolutional Neural Networks. The proposed model is able to not only exploit part context information to preserve object spatial layout structure, but also learn part reliability to emphasize part importance for robust part to target regression. We evaluate the proposed tracker on 4 challenging benchmark sequences, and extensive experimental results demonstrate that our method performs favorably against state-of-the-art trackers because of the powerful capacity of the proposed deep regression model.
大多数现有的基于部件的跟踪方法都是部件到部件的跟踪器，通常有两个分开的步骤，包括部件匹配和目标定位。与现有方法不同，在本文中，我们通过直接从部件推断目标位置，以统一的方式提出了一种新颖的部件到目标（P2T）跟踪器。为了实现这一目标，我们通过卷积神经网络在端到端框架中提出了一种用于部件到目标回归的新颖深度回归模型。所提出的模型不仅能够利用部件上下文信息来保留对象空间布局结构，还能够学习部件可靠性以强调部件对稳健的部件到目标回归的重要性。我们在4个具有挑战性的基准序列上评估了所提出的跟踪器，大量实验结果表明，由于所提出的深度回归模型的强大能力，我们的方法在与当前最先进的跟踪器相比时表现出色。
