The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Recognition of Composite Human Activities through Context-Free Grammar Based Representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
A novel sequence representation for unsupervised analysis of human activities
Artificial Intelligence
Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
Modeling temporal structure of decomposable motion segments for activity classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Actom sequence models for efficient action detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multi-agent event recognition in structured scenarios
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Probabilistic event logic for interval-based event recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human activity prediction: Early recognition of ongoing activities from streaming videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Unsupervised learning of event AND-OR grammar and semantics from video
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Parsing video events with goal inference and intent prediction
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning spatiotemporal graphs of human activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Early prediction of ongoing activity has been more and more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions. Different from early recognition on short-duration simple activities, we propose a novel framework for long-duration complex activity prediction by discovering the causal relationships between constituent actions and the predictable characteristics of activities. The major contributions of our work include: (1) we propose a novel activity decomposition method by monitoring motion velocity which encodes a temporal decomposition of long activities into a sequence of meaningful action units; (2) Probabilistic Suffix Tree (PST) is introduced to represent both large and small order Markov dependencies between action units; (3) we present a Predictive Accumulative Function (PAF) to depict the predictability of each kind of activity. The effectiveness of the proposed method is evaluated on two experimental scenarios: activities with middle-level complexity and activities with high-level complexity. Our method achieves promising results and can predict global activity classes and local action units.