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Support vector machine Adaboost Linear discriminant analysis Linear programming a b s t r a c t Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human–computer interaction and data-driven animation. Deriving an effective facial representation from original face images is(More)
Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual(More)
Matching people across non-overlapping camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and large visual appearance changes caused by variations in view angle, lighting , background clutter and occlusion. To address these challenges, most previous approaches aim to extract visual features(More)
Recovering the shape of any 3D object using multiple 2D views requires establishing correspondence between feature points at different views. However changes in viewpoint introduce self-occlusions, resulting nonlinear variations in the shape and inconsistent 2D features between views. Here we introduce a multi-view nonlinear shape model utilising 2D(More)
Much of recent action recognition research is based on space-time interest points extracted from video using a Bag of Words (BOW) representation. It mainly relies on the dis-criminative power of individual local space-time descrip-tors, whilst ignoring potentially valuable information about the global spatio-temporal distribution of interest points. In this(More)
We develop a novel visual behaviour modelling approach that performs incremental and adaptive model learning for online abnormality detection in a visual surveillance scene. The approach has the following key features that make it advantageous over previous ones: (1) Fully unsupervised learning: both feature extraction for behaviour pattern representation(More)
In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different(More)
This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their drawbacks on accuracy, robust-ness and computational(More)