A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Semantic retrieval of events from indoor surveillance video databases
Pattern Recognition Letters
Primitive Based Action Representation and Recognition
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
A human-centered multiple instance learning framework for semantic video retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Human behavior analysis based on a new motion descriptor
IEEE Transactions on Circuits and Systems for Video Technology
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Semantic classification of human behaviors in video surveillance systems
WSEAS Transactions on Computers
Estimating gaze direction from low-resolution faces in video
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Dynamic events as mixtures of spatial and temporal features
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Real time detection of social interactions in surveillance video
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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In this paper we develop a system for human behaviour recognition in video sequences. Human behaviour is modelled as a stochastic sequence of actions. Actions are described by a feature vector comprising both trajectory information (position and velocity), and a set of local motion descriptors. Action recognition is achieved via probabilistic search of image feature databases representing previously seen actions. A HMM which encodes the rules of the scene is used to smooth sequences of actions. High-level behaviour recognition is achieved by computing the likelihood that a set of predefined Hidden Markov Models explains the current action sequence. Thus, human actions and behaviour are represented using a hierarchy of abstraction: from simple actions, to actions with spatio-temporal context, to action sequences and finally general behaviours. While the upper levels all use (parametric) Bayes networks and belief propagation, the lowest level uses non-parametric sampling from a previously learned database of actions. The combined method represents a general framework for human behaviour modelling. In this paper we demonstrate the results chiefly on broadcast tennis sequences for automated video annotation.