Titlebook: Visual Analysis of Behaviour; From Pixels to Seman Shaogang Gong,Tao Xiang Book 2011 Springer-Verlag London Limited 2011 Activity Recogniti

無(wú)能的人

Modelling Gesture. Automatic interpretation of gesture provides an important means for interaction and communication between human and computer, going beyond the conventional text and graphic based interface. Broadly speaking, human gesture can be composed of movements from any body part of a person, although the mo

尾巴

Action Recognitionlance, video indexing and browsing, and analysis of sporting events. Whilst facial expression and gesture are mainly associated with movement of one or a number of individual body parts, actions are typically associated with whole body movement, for instance, walking, sitting down, riding a horse. A

guzzle

Supervised Learning of Group Activityg or co-existing in a shared space. Group activity modelling is concerned with modelling not only the actions of individual objects in isolation, but also the interactions and causal relationships among individual actions. In order to make semantic sense of visual observations of group activities, a

G-spot

Unsupervised Behaviour Profilingtecting unseen abnormal behaviour patterns whilst recognising novel instances of expected normal behaviour patterns. In this context, an anomaly is defined as an atypical behaviour pattern that is not represented by sufficient examples in previous observations. Behaviour profiling is by unsupervised

EWER

Hierarchical Behaviour Discoveryng behaviour’ to a human observer can be influenced by a wide variety of factors including: (1) the activity of a single object over time; (2) the correlated spatial states of multiple objects, for example, a?piece of abandoned luggage is defined by separation from its owner; and (3) higher order sp

追逐

Learning Behavioural Contextation of behaviour depends largely on knowledge of spatial and temporal context defining where and when it occurs, and correlational context specifying the expectation on the behaviours of correlated other objects in its vicinity. In this chapter, we address the problem of how to model computational

Exhilarate

Modelling Rare and Subtle Behavioursuine interest. This is of practical value because the behaviours of greatest interest for detection are normally rare, for example civil disobedience, shoplifting, driving offenses, and may be intentionally disguised to be visually subtle compared to more obvious ongoing behaviours in a public space

Instrumental

Man in the Loopes reflect normal behaviours. Unusual behaviours, either because of being rare or abnormal, only constituent a small portion in the observed data. Whilst an unsupervised learning based model can be constructed to detect unusual behaviours through a process of outlier detection, an outlier detection