We present an online visual tracking algorithm by managing multiple target
appearance models in a tree structure. The proposed algorithm employs
Convolutional Neural Networks (CNNs) to represent target appearances, where
multiple CNNs collaborate to estimate target states and determine the desirable
paths for online model updates in the tree. By maintaining multiple CNNs in
diverse branches of tree structure, it is convenient to deal with
multi-modality in target appearances and preserve model reliability through
smooth updates along tree paths. Since multiple CNNs share all parameters in
convolutional layers, it takes advantage of multiple models with little extra
cost by saving memory space and avoiding redundant network evaluations. The
final target state is estimated by sampling target candidates around the state
in the previous frame and identifying the best sample in terms of a weighted
average score from a set of active CNNs. Our algorithm illustrates outstanding
performance compared to the state-of-the-art techniques in challenging datasets
such as online tracking benchmark and visual object tracking challenge.

本文提出了一种在线视觉跟踪算法，使用卷积神经网络来表示目标外观，并通过在树形结构中管理多个目标外观模型来实现目标跟踪。通过在树的不同分支中维护多个卷积神经网络，可以方便地处理目标外观的多样性，并通过沿树路径进行平滑更新来保持模型的可靠性。最终的目标状态通过在前一帧周围采样目标候选状态并在一组活动的卷积神经网络中识别最佳样本来估计。与现有技术相比，我们的算法在在线跟踪基准和视觉对象跟踪挑战等具有挑战性的数据集上表现出色。