Deep tracking in the wild: End-to-end tracking using recurrent neural networks
Deep tracking in the wild: End-to-end tracking using recurrent neural networks
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DOI：
10.1177/0278364917710543
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发表时间：
2018-04-01
影响因子：
9.2
通讯作者：
Posner, Ingmar
中科院分区：
文献类型：
--
作者：
Dequaire, Julie;Ondruska, Peter;Posner, Ingmar
本文提出了一种新的方法来跟踪静态和动态的目标，自主车辆在复杂的城市环境中运行。虽然传统的跟踪方法通常具有许多手工设计的阶段，但这种方法是端到端学习的，可以直接从原始激光输入预测完全无遮挡的占用网格。我们使用一个递归神经网络来捕捉环境的状态和演化，并以完全无监督的方式训练模型。在这样做时，我们的用例与无模型的多对象跟踪相比，尽管我们没有显式地执行底层数据关联过程。此外，我们证明了跟踪任务的底层表示可以通过感应传输来利用，以数据高效的方式训练对象检测器。我们激发了一些建筑功能，并显示了积极的贡献，扩张卷积，动态和静态记忆单元的任务，跟踪和分类复杂的动态场景，通过完全闭塞。我们的实验结果说明了该模型的能力，跟踪汽车，公共汽车，行人和骑自行车的人从移动和静止的平台。此外，我们将该方法与更传统的无模型多对象跟踪管道进行了比较和对比，证明它可以更准确地从当前输入预测对象的未来状态。
This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a fully unoccluded occupancy grid from raw laser input. We employ a recurrent neural network to capture the state and evolution of the environment, and train the model in an entirely unsupervised manner. In doing so, our use case compares to model-free, multi-object tracking although we do not explicitly perform the underlying data-association process. Further, we demonstrate that the underlying representation learned for the tracking task can be leveraged via inductive transfer to train an object detector in a data efficient manner. We motivate a number of architectural features and show the positive contribution of dilated convolutions, dynamic and static memory units to the task of tracking and classifying complex dynamic scenes through full occlusion. Our experimental results illustrate the ability of the model to track cars, buses, pedestrians, and cyclists from both moving and stationary platforms. Further, we compare and contrast the approach with a more traditional model-free multi-object tracking pipeline, demonstrating that it can more accurately predict future states of objects from current inputs.