Face recognition from video

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence "reillumination" algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature. © Springer-Verlag Berlin Heidelberg 2006.

Video tracking using dual-treewavelet polar matching and particle filtering

In this paper, we describe a video tracking application using the dual-tree polar matching algorithm. The models are specified in a probabilistic setting, and a particle ilter is used to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to track a simulated video moving target in an urban environment with complete and partial occlusions. © The Institution of Engineering and Technology.

3D multi-car tracking based on monocular video

We propose a system that can reliably track multiple cars in congested traffic environments. Our system's key basis is the implementation of a sequential Monte Carlo algorithm, which introduces robustness against problems arising due to the proximity between vehicles. By directly modelling occlusions and collisions between cars we obtain promising results on an urban traffic dataset. Extensions to this initial framework are also suggested. © 2010 IEEE.

Semi-supervised video segmentation using tree structured graphical models

We present a novel, implementation friendly and occlusion aware semi-supervised video segmentation algorithm using tree structured graphical models, which delivers pixel labels alongwith their uncertainty estimates. Our motivation to employ supervision is to tackle a task-specific segmentation problem where the semantic objects are pre-defined by the user. The video model we propose for this problem is based on a tree structured approximation of a patch based undirected mixture model, which includes a novel time-series and a soft label Random Forest classifier participating in a feedback mechanism. We demonstrate the efficacy of our model in cutting out foreground objects and multi-class segmentation problems in lengthy and complex road scene sequences. Our results have wide applicability, including harvesting labelled video data for training discriminative models, shape/pose/articulation learning and large scale statistical analysis to develop priors for video segmentation. © 2011 IEEE.

The double dance of agency: a socio-theoretic account of how machines and humans interact

The nature of the relationship between information technology (IT) and organizations has been a long-standing debate in the Information Systems literature. Does IT shape organizations, or do people in organisations control how IT is used? To formulate the question a little differently: does agency (the capacity to make a difference) lie predominantly with machines (computer systems) or humans (organisational actors)? Many proposals for a middle way between the extremes of technological and social determinism have been put advanced; in recent years researchers oriented towards social theories have focused on structuration theory and (lately) actor network theory. These two theories, however, adopt different and incompatible views of agency. Thus, structuration theory sees agency as exclusively a property of humans, whereas the principle of general symmetry in actor network theory implies that machines may also be agents. Drawing on critiques of both structuration theory and actor network theory, this paper develops a theoretical account of the interaction between human and machine agency: the double dance of agency. The account seeks to contribute to theorisation of the relationship between technology and organisation by recognizing both the different character of human and machine agency, and the emergent properties of their interplay.

Spread transform watermarking for video sources

Spread Transform (ST) is a quantization watermarking algorithm in which vectors of the wavelet coefficients of a host work are quantized, using one of two dithered quantizers, to embed hidden information bits; Loo had some success in applying such a scheme to still images. We extend ST to the video watermarking problem. Visibility considerations require that each spreading vector refer to corresponding pixels in each of several frames, that is, a multi-frame embedding approach. Use of the hierarchical complex wavelet transform (CWT) for a visual mask reduces computation and improves robustness to jitter and valumetric scaling. We present a method of recovering temporal synchronization at the detector, and give initial results demonstrating the robustness and capacity of the scheme.

The evolution of information while building cross-domain models of a design: A video experiment

Models capturing the connectivity between different domains of a design, e.g. between components and functions, can provide a tool for tracing and analysing aspects of that design. In this paper, video experiments are used to explore the role of cross-domain modelling in building up information about a design. The experiments highlight that cross-domain modelling can be a useful tool to create and structure design information. Findings suggest that consideration of multiple domains encourages discussion during modelling, helps identify design aspects that might otherwise be overlooked, and can help promote consideration of alternative design options. Copyright © 2002-2012 The Design Society. All rights reserved.

Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.