Stephen J. McKenna

Tracking Groups of People

A computer vision system for tracking multiple people in relatively unconstrained environments is described. Tracking is performed at three levels of abstraction: regions, people, and groups. A novel, adaptive background subtraction method that combines color and gradient information is used to cope with shadows and unreliable color cues. People are tracked through mutual occlusions as they form groups and separate from one another. Strong use is made of color information to disambiguate occlusion and to provide qualitative estimates of depth ordering and position during occlusion. Simple interactions with objects can also be detected. The system is tested using both indoor and outdoor sequences. It is robust and should provide a useful mechanism for bootstrapping and reinitialization of tracking using more specific but less robust human models.

Tracking colour objects using adaptive mixture models

The use of adaptive Gaussian mixtures to model the colour distributions of objects is described. These models are used to perform robust, real-time tracking under varying illumination, viewing geometry and camera parameters. Observed log-likelihood measurements were used to perform selective adaptation.

MODELLING FACIAL COLOUR AND IDENTITY WITH GAUSSIAN MIXTURES

Abstract An integrated system for the acquisition, normalisation and recognition of moving faces in dynamic scenes is introduced. Four face recognition tasks are defined and it is argued that modelling person-specific probability densities in a generic face space using mixture models provides a technique applicable to all four tasks. The use of Gaussian colour mixtures for face detection and tracking is also described. Results are presented using data from the integrated system.

Tracking and segmenting people in varying lighting conditions using colour

Colour cues were used to obtain robust detection and tracking of people in relatively unconstrained dynamic scenes. Gaussian mixture models were used to estimate probability densities of colour for skin, clothing and background. These models were used to detect, track and segment people, faces and hands. A technique for dynamically updating the models to accommodate changes in apparent colour due to varying lighting conditions was used. Two applications are highlighted: (1) actor segmentation for virtual studios, and (2) focus of attention for face and gesture recognition systems. A system implemented on a 200 MHz PC tracks multiple objects in real time.

Colour Model Selection and Adaption in Dynamic Scenes

We use colour mixture models for real-time colour-based object localisation, tracking and segmentation in dynamic scenes. Within such a framework, we address the issues of model order selection, modelling scene background and model adaptation in time. Experimental results are given to demonstrate our approach in different scale and lighting conditions.

Tracking interacting people

A computer vision system for tracking multiple people in relatively unconstrained environments is described. Tracking is performed at three levels of abstraction: regions, people and groups. A novel, adaptive background subtraction method that combines colour and gradient information is used to cope with shadows and unreliable colour cues. People are tracked through mutual occlusions as they form groups and part from one another. Strong use is made of colour information to disambiguate occlusions and to provide qualitative estimates of depth ordering and position during occlusion. Some simple interactions with objects can also be detected. The system is tested using indoor and outdoor sequences. It is robust and should provide a useful mechanism for bootstrapping and reinitialisation of tracking using more-specific but less-robust human models.

Tracking Facial Feature Points with Gabor Wavelets and Shape Models

A feature-based approach to tracking rigid and non-rigid facial motion is described. Feature points are characterised using Gabor wavelets and can be individually tracked by phase-based displacement estimation. In order to achieve robust tracking a flexible shape model is used to impose global constraints upon the local feature points and to constrain the tracker. While there are many applications in facial analysis, the approach can be used for tracking other textured objects.

Segmentation and Tracking Using Color Mixture Models

A system is described that provides robust and real-time focus-of-attention for tracking and segmentation of multi-coloured objects. Gaussian mixture models were used to estimate the probability densities of object foreground and scene background colours. Tracking was performed by fitting dynamic bounding boxes to image regions of maximum probability. Two scenarios are presented: (1) real-time face tracking based upon a skin colour model and (2) dynamic body segmentation for virtual studios based upon combined foreground and background models.

An investigation into face pose distributions

Visual perception of faces is invariant under many transformations, perhaps the most problematic of which is pose change (face rotating in depth). We use a variation of Gabor wavelet transform (GWT) as a representation framework for investigating face pose measurement. Dimensionality reduction using principal components analysis (PCA) enables pose changes to be visualised as manifolds in low-dimensional subspaces and provides a useful mechanism for investigating these changes. The effectiveness of measuring face pose with GWT representations was examined using PCA. We discuss our experimental results and draw a few preliminary conclusions.

Real-time face pose estimation

Abstract Methods were investigated for estimating the poses of human faces undergoing large rotations in depth. Dimensionality reduction using principal components analysis enabled pose changes to be visualised as manifolds in low-dimensional subspaces and provided a useful mechanism for investigating these changes. Appearance-based matching using Gabor wavelets was developed for real-time face tracking and pose estimation. A real-time Gabor wavelet projection was implemented using a Datacube MaxVideo 250 whilst an alternative system for real-time pose estimation used only standard PC hardware.