Pedestrian Tracking with Gated Recurrent Units and Attention Mechanisms

TL;DR

This paper introduces a deep learning approach using Gated Recurrent Units and attention mechanisms for pedestrian tracking, aiming to improve accuracy over traditional methods like PDR by leveraging synchronized sensor and LIDAR data.

Contribution

The paper presents a novel deep learning model for pedestrian tracking that incorporates attention mechanisms and a new data collection apparatus for synchronized IMU and LIDAR data.

Findings

Preliminary results show promising accuracy improvements.

The proposed method reduces accumulation errors compared to PDR.

A new dataset with synchronized IMU and LIDAR data was developed.

Abstract

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.