Vision and Signal Processing

Project summary

Aviation is a major economic driver both within Australia and overseas, but the aviation industry faces growing challenges from the increase in passengers and changing regulations. To meet these challenges, airports, airlines, government agencies and others need to maximise their efficiency and productivity. However, complex dependencies and differing operational objectives complicate this task.

This project aims to develop a real-time, whole-of-system operational performance framework that can help operators in finding and evaluating solutions to maximise throughput, reduce wait times and mitigate flow-on effects. Innovative new video analytic and Bayesian Network based tools are integrated to address the challenges of adaptability and uncertainty.

Project summary

During airport security screening and processing, confusion and error are greatest when systems or contexts are unfamiliar. Poorly designed systems compromise the interactions of airport security personnel and decrease their ability to promptly and accurately respond to situations.

This project aims to deliver a suite of automated methods to monitor security operator knowledge and engagement, to assess the real-time security screening context, and to detect unusual passenger behavior at the screening check-point. This monitoring aims to provide new knowledge and techniques to enhance security operator performance, refine the screening process, improve passenger experience and, most critically, ensure safety at Australian airports.

Project summary

The last three decades have seen the field of computer vision make tremendous advancements in the area of structure from motion.

Despite this progress, the reality is that current approaches are unable to accurately and densely reconstruct the most important object we care about: people.

This project will create dense 3D reconstruction techniques which can manage non-rigid human anatomy using only 2D images from video sequences and medical imaging sources, such as X-rays.

We will also create these models in one shot from a single image.

Project summary

The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive.

Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than aiming to achieve invariance to it. The project will also research new data agnostic feature compaction capabilities to enable scalable learning on the world's largest and challenging expression dataset available to us through international collaboration. Tackling these two major open problems will make accurate coding of facial expressions in natural environments achievable.

Project summary

In vegetable and fruit production, the cost of manual labor related to harvesting can be significant to farm operations.

This project can potentially enable growers managed more efficiently their workforce and the harvesting with improved situational awareness of quantity, quality and spacial variability of the crops.

This project seeks to develop data analytics to provide capsicum growers with situational awareness of crops based on multi-spectral robotic-visual data.

This is a research component conducted by QUT as part of a project led by the Queensland Government in collaboration with QUT and CSIRO and funded by Horticulture Innovation Australia.