Finding Dense Locations in Indoor Tracking Data

Abstract

Finding the dense locations in large indoor spaces is very useful for getting overloaded locations, security, crowd management, indoor navigation, and guidance. Indoor tracking data can be very large and are not readily available for finding dense locations. This paper presents a graph-based model for semi-constrained indoor movement, and then uses this to map raw tracking records into mapping records representing object entry and exit times in particular locations. Then, an efficient indexing structure, the Dense Location Time Index (DLT-Index) is proposed for indexing the time intervals of the mapping table, along with associated construction, query processing, and pruning techniques. The DLT-Index supports very efficient aggregate point queries, interval queries, and dense location queries. A comprehensive experimental study with real data shows that the proposed techniques can efficiently find dense locations in large amounts of indoor tracking data.