Fundamentals of speech recognition
Fundamentals of speech recognition
A State-Based Approach to the Representation and Recognition of Gesture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Derivation of Primitives for Movement Classification
Autonomous Robots
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Behaviour Understanding in Video: A Combined Method
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
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There has been a recent interest in segmenting action sequences into meaningful parts (action primitives) and to model actions on a higher level based on these action primitives. Unlike previous works where action primitives are defined a-priori and search is made for them later, we present a sequential and statistical learning algorithm for automatic detection of the action primitives and the action grammar based on these primitives. We model a set of actions using a single HMM whose structure is learned incrementally as we observe new types. Actions are modeled with sufficient number of Gaussians which would become the states of an HMM for an action. For different actions we find the states that are common in the actions which are then treated as an action primitive.