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

discord

浮雕寶石

Understanding Facial Expressionvariations, but also maximise differences between two different types of expressions, known as between-class variations. If indiscriminative image features are selected for a representation, it is difficult to achieve good recognition regardless the choice of a classification mechanism. In this chap

過多

情感脆弱

Action Recognitionmed, representation and modelling of actions differ from those for facial expression and gesture. In this chapter, we study three different approaches to modelling and interpreting actions when observed under different viewing conditions. These conditions range from a relatively static scene against

LVAD360

GAVEL

Unsupervised Behaviour Profilingause manual labelling of behaviour patterns is often impractical given the vast amount of video data, and is subject to inconsistency and error prone. The method performs incremental learning to cope with changes of behavioural context. It also detects anomalies on-line so that (a decision on whethe

GLUE

使?jié)M足

Learning Behavioural Contextrs of certain characteristics are expected in one region but differ from those observed in other regions. Behaviour correlational context specifies how the interpretation of a behaviour can be affected by behaviours of other objects either nearby in the same semantic region or further away in other

忙碌

Modelling Rare and Subtle Behavioursith a behaviour of interest captured in video data, and a few more pixels differentiating a rare behaviour from a typical one. To eliminate the prohibitive manual labelling cost, both in time and inconsistency, required by traditional supervised methods, we describe a weakly supervised framework, in

國家明智