Researchers from the University of Wollongong have successfully demonstrated an algorithm that detects fights through CCTV cameras on public transport. The team, based at the SMART Infrastructure Facility, has successfully demonstrated the ability of their software to detect a fight using actual footage. Next, they will stage a “fight” at Wollongong station and test the ability of the CCTV network and operators to act on a violent incident.
Lead researcher Dr John Barthelemy said this was the first time that this type of artificial intelligence would be used by a public transport operator. The project, which started in September 2020, was one of four winners of a Safety After Dark challenge created by Transport for NSW.
The challenge focuses on making women feel, and be safer when travelling on public transport. Research into women’s safety revealed that girls and women do not always feel safe travelling in the city at night.
“The next stage will be like moving from the lab to the real world,” Dr Barthelemy said. “We want to test how easy it is to deploy it in a station – we know that the core of it works but we want to test all the things around it.”
The software is designed to automatically analyse real-time camera feeds and alert an operator when it detects a suspicious incident or an unsafe environment. The data and reports automatically generated by the software can then be used to help prevent the abuse and violence committed towards women after dark in public transportation.
The software uses open-source code to predict a fight by looking at typical human poses. A human controller who accepts or rejects the suggestion then reviews footage. The result is then used by the software to analyse images with greater accuracy.
One of the largest research institutions in the world dedicated to helping governments and businesses better plan for the future, SMART brings together experts from fields such as rail, infrastructure systems, transport, water, energy, economics and modelling and simulation, providing 30 state-of-the-art laboratories to facilitate this important research.
More projects from SMART’s research facility
SMART Cities and Transport uses a wide range of tools, like modelling, optimisation, simulation and data analytics to create the cities of tomorrow by addressing the challenges of today. The SMART Cities and Transport research theme comprise of two research groups:
- Smart Cities & Communities
The Smart Cities & Communities research group uses data-driven models and multi-disciplinary approaches to explore how cities can create more liveable neighbourhoods, open spaces and workplaces.
The Smart Cities & Communities group aims to provide data-driven analysis and modelling on the liveability metrics of the region’s cities. Areas covered include parks and green space, tree coverage, sanitation and recycling, health and well-being, walking and cycling promotion, transit opportunities, safety and accident prevention, housing and workplace balance, or other metrics as chosen by collaborators.
The creation of Smart Cities is a dynamic area of study, and one that will change as technology improves and citizens see the benefit of data gathering. Over time, the technology to measure will improve, citizens may wish for other options, and the entire work package will be reinvestigated. The Group aims to be both responding to, and these changes in the cities of tomorrow.
- Future Transport & Mobility
The Future Transport & Mobility research group focuses on the rapidly growing area of next-generation transport system and urban mobility.
The Group aims to reshape people’s travel behaviour and practically improve transport systems in the era of connectivity, sharing, big data and smart cities by using a wide range of advanced tools, like modelling, optimisation, simulation and data analytics.
One particular focus of the research group is to facilitate electric vehicle (EV) uptake in Australia (much lower than other developed countries) by providing optimal vehicle operation strategies and corresponding infrastructure planning. Other areas of research include Traveller Choice Behaviour Modelling, Transport Network Modelling and Optimisation, Optimisation of Transport Systems using Data-driven Approach, and Smart Cities with Smart and Sustainable Transport.
