Adaptive learning visual sensor networks for crowd modelling

Project description

The prevalence of camera networks for surveillance, together with the decreasing cost of infrastructure, has produced a significant demand for robust monitoring systems. Current systems offer limited functionality, particularly in their reliance on centralised processing of gathered information. This project addresses end-to-end system challenges of wireless visual sensor networks. Integrating developments across the spatial, spatio-temporal and decision domains, the project will incorporate distributed sensor network technology with intelligent fusion of information, to deliver unique long-term behaviour analysis capabilities for efficient planning in highly crowded environments.

Project team

Leader: Jayavardhana Gubbi Lakshminarasimha

Staff: Marimuthu Palaniswami, Slaven Marusic

Students: Aravinda Rao

Collaborators: Ba-Ngu Vo (University of Western Australia), Paul Stanley (ARUP), Andrew Maher (ARUP), Trevor Dohnt (Melbourne Cricket Club), Subhash Challa (SenSen Pty Ltd)

Sponsors: ARC Linkage Grant, ARUP, Melbourne Cricket Club, SenSen Networks Pty Ltd

Other projects

Networks and data in society projects

Research Centre

ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)


Electrical & Electronic Engineering


Networks and data in society


artificial intelligence; machine learning; sensor networks; signals and systems