Predicting human activities using spatio-temporal structure of interest points
Proceedings of the 20th ACM international conference on Multimedia
Modeling complex temporal composition of actionlets for activity prediction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Learning human interaction by interactive phrases
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Propagative hough voting for human activity recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Learning to recognize unsuccessful activities using a two-layer latent structural model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
Egocentric activity monitoring and recovery
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Exploring dense trajectory feature and encoding methods for human interaction recognition
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Online human gesture recognition from motion data streams
Proceedings of the 21st ACM international conference on Multimedia
Knives are picked before slices are cut: recognition through activity sequence analysis
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
Ongoing human action recognition with motion capture
Pattern Recognition
Spatio-temporal layout of human actions for improved bag-of-words action detection
Pattern Recognition Letters
Machine Vision and Applications
Max-Margin Early Event Detectors
International Journal of Computer Vision
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In this paper, we present a novel approach of human activity prediction. Human activity prediction is a probabilistic process of inferring ongoing activities from videos only containing onsets (i.e. the beginning part) of the activities. The goal is to enable early recognition of unfinished activities as opposed to the after-the-fact classification of completed activities. Activity prediction methodologies are particularly necessary for surveillance systems which are required to prevent crimes and dangerous activities from occurring. We probabilistically formulate the activity prediction problem, and introduce new methodologies designed for the prediction. We represent an activity as an integral histogram of spatio-temporal features, efficiently modeling how feature distributions change over time. The new recognition methodology named dynamic bag-of-words is developed, which considers sequential nature of human activities while maintaining advantages of the bag-of-words to handle noisy observations. Our experiments confirm that our approach reliably recognizes ongoing activities from streaming videos with a high accuracy.