MUSIT
MUlti-Sensor Inferred Trajectories
Site: https://cordis.europa.eu/project/id/101182585
Contacts
Abstract
The abundance of tracking sensors in recent years has led to the
generation of high-frequency and high-volume streams of data, including
vessels, vehicles' tracking data, smartwatches, cameras, and earth
observation sensors. However, there are cases where the trajectory of a
moving object has gaps, errors, or is unavailable. However, a vast pool
of tracking data is available but remains unexplored or underutilized
and has the potential to reveal important information. The MUlti-Sensor
Inferred Trajectories (MUSIT) project aims at exploring and fusing data
from all heterogeneous sources to provide detailed information about a
moving object’s whereabouts and behavior, reduce gaps, and produce a
refined and inferred trajectory with minimal errors. The fusion of
multi-sensor data is required to fill in the trajectory gaps of moving
objects and attach useful semantics to the trajectory and its
components. AI algorithms and spatio-temporal methodologies that can
fuse information and infer the “missing knowledge” are crucial to the
implementation of MUSIT. Furthermore, different representation models
from multiple domains within the ICT sector will also be explored.
Datasets will be made available in cases where it was previously thought
impossible, and infer knowledge thus improving the overall
surveillance. Therefore, the MUSIT project will tackle the
aforementioned issues in a process that can be categorized into three
parts: i) data collection and creation, ii) exploitation and utilization
of cross-domain representation models within the ICT sector for
trajectories, and iii) analysis and processing of outcomes to produce
information-rich results related to vessel monitoring and urban
mobility.
Duration
48 Months
Financial Institution
Unione Europea