Class-agnostic object tracking is particularly difficult in cluttered
environments as target specific discriminative models cannot be learned a
priori. Inspired by how the human visual cortex employs spatial attention and
separate "where" and "what" processing pathways to actively suppress irrelevant
visual features, this work develops a hierarchical attentive recurrent model
for single object tracking in videos. The first layer of attention discards the
majority of background by selecting a region containing the object of interest,
while the subsequent layers tune in on visual features particular to the
tracked object. This framework is fully differentiable and can be trained in a
purely data driven fashion by gradient methods. To improve training
convergence, we augment the loss function with terms for a number of auxiliary
tasks relevant for tracking. Evaluation of the proposed model is performed on
two datasets: pedestrian tracking on the KTH activity recognition dataset and
the more difficult KITTI object tracking dataset.

本文提出了一种基于分层注意力循环模型的目标跟踪算法，该算法可以在视频中跟踪单个物体，并通过梯度方法进行完全可微分且纯数据驱动的训练。为了改进训练收敛性，在损失函数中增加了与跟踪相关的辅助任务项。该模型在 KTH 活动识别数据集和 KITTI 物体跟踪数据集上进行了评估。