Research Projects

Current Projects

  • Real-time Internet of Things
  • Automated classification of stroke-affected subjects with accelerometer data based on NIHSS scores
  • Crack detection from videos/images

Camera based Assistive Technology for Vision-Impaired Mobility

vision-laser Our approach uses a novel feature descriptor dubbed Histogram of Intersections (HoI) to detect potholes and uneven surfaces. The system uses Two different types of laser patterns (mesh and cross-hair) are projected on to surfaces (such as roads, stairs, pedestrian pathways) and using a GoPro camera, video of the projected laser patterns are recorded. Patterns are detected based on number of laser grid (intersections) and strength of the laser pattern intensities.

IoT-based Real-time Urban Microclimate Monitoring

IoT-urban-microclimate This work introduces an integrated geovisualization framework, built for real-time wireless sensor network data on the synergy of computational intelligence and visual methods, to analyze complex patterns of urban microclimate. A Bayesian maximum entropy based method and a hyperellipsoidal model based algorithm have been build in our integrated framework undertand how tree canopy cover can affect urban microclimate environment using IoT and heterogeneous sensor data.

Detecting Crowd Motion Events from Videos

crowd-event-detecion-optical-flow This work introduces a new framework based on optical flow manifolds (OFM) to detect crowd events. Essentially, the events are recognized from optical flow vector on a manifold. Experiment results suggest that the proposed semi-supervised approach performs best in detecting merging, separating into group (“splitting”), and dispersion events compared with existing methods. The advantages of the semi-supervised approach are the requirement of a single parameter to detect crowd events, and results that are provided on a frame-by-frame basis.
Grant: ARC LP100200430

Detecting Loitering Behaviour in Crowded Scenes

loitering-detection From a crowd management/surveillance viewpoint, it is important to have automated tools to detect loitering people. This could be individuals or group of people loitering in and around an area in public spaces. Or it could be people who appear to be suspicious. This work provides a framework to detect and track patterns of loitering/suspicious individuals. The framework uses video as input and outputs individuals marked with possibly suspicious or exhibit loitering behaviour.
Grant: ARC LP100200430

Design of Low-cost Water Quality Monitoring System

water-quality-monitoring Water pollution remains a key factor contributing to declining ecological health in aquatic ecosystems worldwide. In Australia, the Victoria state is facing a major challenge in maintaining water quality in the freshwater systems. In this regard, we developed a prototype sensor system as one component of the Autonomous Live Animal Response Monitor (ALARM) currently under development at the Victorian Center for Aquatic Pollution Identification and Management (CAPIM). The system measures temperature, light in- tensity, pH, electrical conductivity (EC), total dissolved solids (TDS), salinity (SAL), dissolved oxygen (DO) and oxidation reduction potential (ORP). This system was designed to measure a suite of biologically relevant physiochemical parameters in freshwater.

Estimating Crowd Density from Videos

crowd-density-estimation-optical-flow Growing population and urbanization has mobilized the day-to day activities, and requires improved safety and security tools. Crowd density estimationdeals with estimation of number of people per given area in a 2D scene. Accurate density estimation assists security personnel and crowd managers to have a knowledge of the ground situation in tine event of untoward incidents. However, automated crowd density estimation techniques/tools often face difficulties in detecting motion from the scene due to varying environmental conditions, occlusion and crowded scenes. Instead of detecting and tracking individual person, density estimation is an approximate method to count people. This work presents a semi-supervised approach using optical flow features and clustering those features to represent density in a 2D area.
Grant: ARC LP100200430

Real-time Sensing and Asset Tracking

real-time-asset-tracking-GPS For asset management. fleet management and logistics, it is necessary to track assets, including trucks, cars, in real time. The project involved developing hardware, firmware and embedded system to acquire, process and send GPS locations to a centralized server through SMS. The platform developed is scalable to generic sensor networking platform having numerous applications ranging from medical to military and from aerospace to underwater applications. Figure (Courtesy: Google Maps) shows the path traced by a vehicle in real time from the developed system.