Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Derivation of Primitives for Movement Classification
Autonomous Robots
Analysis of Object Interactions in Dynamic Scenes
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Recognizing multitasked activities from video using stochastic context-free grammar
Eighteenth national conference on Artificial intelligence
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Towards the Computational Perception of Action
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Reconstructing force-dynamic models from video sequences
Artificial Intelligence
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning, detection and representation of multi-agent events in videos
Artificial Intelligence
Boosted string representation and its application to video surveillance
Pattern Recognition
CASEE: a hierarchical event representation for the analysis of videos
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Detecting and discriminating behavioural anomalies
Pattern Recognition
Shopping behavior recognition using a language modeling analogy
Pattern Recognition Letters
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Manipulations are a significant subset of human gestures that are distinguished by the fact that their logic and meaning are particularly clear, being heavily constrained by physical causality. We present techniques and causal semantics for interpreting video of manipulation tasks such as disassembly. Psychologically-based causal constraints are used to detect meaningful changes in the integrity and motions of foreground segmented blobs; a small causal model of manipulation is used to disambiguate and parse these into a coherent account of video's action. The causal constraints are drawn from studies of infant perceptual development; as with infants, they precede and may possibly even bootstrap the ability to reliably segment still objects. Our implementation produces a script of the causal evolution of the scene-output that supports cartoon summary, automated editing, and higher-level reasoning.