论文标题
带有封闭式复发单元和注意力机制的行人跟踪
Pedestrian Tracking with Gated Recurrent Units and Attention Mechanisms
论文作者
论文摘要
长期以来，行人跟踪一直被认为是一个重要的问题，尤其是在安全应用程序中。以前，已经提出了许多使用各种类型的传感器的方法。一种流行的方法是基于惯性测量单元（IMU）传感器的行人死计算（PDR）[1]。但是，PDR是一种基于积分和阈值的方法，它遭受累积错误和较低的精度。在本文中，我们提出了一种新颖的方法，其中传感器数据被馈入一个深度学习模型，以预测行人的位移和方向。我们还设计了一种新的设备，以收集和构建包含同步IMU传感器数据和通过LIDAR测量的精确位置的数据库。初步结果是有希望的，我们计划通过收集更多数据并为所有一般行人动作调整深度学习模型来推动这一点。
Pedestrian tracking has long been considered an important problem, especially in security applications. Previously,many approaches have been proposed with various types of sensors. One popular method is Pedestrian Dead Reckoning(PDR) [1] which is based on the inertial measurement unit(IMU) sensor. However PDR is an integration and threshold based method, which suffers from accumulation errors and low accuracy. In this paper, we propose a novel method in which the sensor data is fed into a deep learning model to predict the displacements and orientations of the pedestrian. We also devise a new apparatus to collect and construct databases containing synchronized IMU sensor data and precise locations measured by a LIDAR. The preliminary results are promising, and we plan to push this forward by collecting more data and adapting the deep learning model for all general pedestrian motions.
