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Pedestrian tracking has long been considered an important problem, especiallyin security applications. Previously,many approaches have been proposed withvarious types of sensors. One popular method is Pedestrian Dead Reckoning(PDR)[1] which is based on the inertial measurement unit(IMU) sensor. However PDR isan integration and threshold based method, which suffers from accumulationerrors and low accuracy. In this paper, we propose a novel method in which thesensor data is fed into a deep learning model to predict the displacements andorientations of the pedestrian. We also devise a new apparatus to collect andconstruct databases containing synchronized IMU sensor data and preciselocations measured by a LIDAR. The preliminary results are promising, and weplan to push this forward by collecting more data and adapting the deeplearning model for all general pedestrian motions.

Pedestrian Tracking with Gated Recurrent Units and Attention Mechanisms