A REAL-TIME IMPROVED PEDESTRIAN DEAD RECKONING TRAJECTORY TRACKING ALGORITHM

Abstract

Accurate location of pedestrians plays a crucial role in emergency relief, traffic control, crowd behavior analysis and other aspects. Especially in the context of the COVID-19 pandemic in recent years, pedestrian location technology can help relevant departments to complete target screening more quickly.However, the Pedestrian Dead Reckoning algorithm can only calculate the target trajectory through the sensor return value, but can not carry out real-time trajectory correction and location.With the rapid development of deep learning, object detection and tracking technology based on computer vision has been applied to pedestrian location, but there are two challenges in the application process.Firstly, in the pedestrian gathering scene, the target number base is large, so the accuracy of the current detection algorithm needs to be improved and the model drift index of the tracking algorithm needs to be reduced. Secondly, there is a certain distortion between the real three-dimensional coordinate space of pedestrians and the two-dimensional image captured by the camera, and the transformation of the spatial coordinate of the target point is a technical difficulty.In this regard, first of all, to improve the accuracy of pedestrian target detection in crowded scenes, this paper adopts the method of improving the generalization of network to pedestrian target, and uses k-means algorithm to find the best prior frame of pedestrian, and sets the width to height ratio suitable for the target.Secondly, to solve the problem of model drift in the above tracking process, this paper proposes a binary classification model based on target appearance difference, which introduces target context information as a new target distinguishing feature when two or more targets are similar.Finally, in order to obtain more accurate coordinate position information, this paper combines the inverse perspective algorithm to calculate the target coordinates into the coordinates in the world coordinate system, and calculates the exact position of the target in the aerial view, as well as the distance between the targets or the current flow of people.In order to evaluate the effectiveness of the proposed algorithm, experiments on target detection, tracking and precise positioning were carried out in different intensity scenarios to verify the feasibility of the proposed method.