Identifying Stops and Moves in WiFi Tracking Data.

Cristian Chilipirea,Mitra Baratchi,Maarten Steen,Ciprian Dobre

doi:10.3390/s18114039

Abstract

There are multiple methods for tracking individuals, but the classical ones such as using GPS or video surveillance systems do not scale or have large costs. The need for large-scale tracking, for thousands or even millions of individuals, over large areas such as cities, requires the use of alternative techniques. WiFi tracking is a scalable solution that has gained attention recently. This method permits unobtrusive tracking of large crowds, at a reduced cost. However, extracting knowledge from the data gathered through WiFi tracking is not simple, due to the low positional accuracy and the dependence on signals generated by the tracked device, which are irregular and sparse. To facilitate further data analysis, we can partition individual trajectories into periods of stops and moves. This abstraction level is fundamental, and it opens the way for answering complex questions about visited locations or even social behavior. Determining stops and movements has been previously addressed for tracking data gathered using GPS. GPS trajectories have higher positional accuracy at a fixed, higher frequency as compared to trajectories obtained through WiFi. However, even with the increase in accuracy, the problem, of separating traces in periods of stops and movements, remains similar to the one we encountered for WiFi tracking. In this paper, we study three algorithms for determining stops and movements for GPS-based datasets and explore their applicability to WiFi-based data. We propose possible improvements to the best-performing algorithm considering the specifics of WiFi tracking data.

Highlights

Making sense of crowd tracking data is far from trivial
Extracting knowledge from the data gathered through WiFi tracking is not simple, due to the low positional accuracy and the dependence on signals generated by the tracked device, which are irregular and sparse
The effect was even stronger if the individual stopped in one such location, creating movement periods of one hour that were labeled as stops in the ground truth list

Summary

Introduction

Making sense of crowd tracking data is far from trivial. Individuals can have unique movement behaviors, while some crowd-level characteristics can be maintained. It is even more difficult to make sense of this type of data when positions are approximate and detections are sparse. We can separate them into periods of stops and moves [1,2]. This is a fundamental step that can be used to answer many questions that would be intangible given the raw dataset. A few possible questions are: “What are the most interesting locations?”, “How many people are traveling in pairs or small groups?”

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Conclusion