A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
State Transition Analysis: A Rule-Based Intrusion Detection Approach
IEEE Transactions on Software Engineering
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Modeling protein families using probabilistic suffix trees
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
An Analysis of Some Graph Theoretical Cluster Techniques
Journal of the ACM (JACM)
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Bayesian Object Localisation in Images
International Journal of Computer Vision
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
a CAPpella: programming by demonstration of context-aware applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
An APRIORI-based Method for Frequent Composite Event Discovery in Videos
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Robust recognition of physical team behaviors using spatio-temporal models
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning and inferring transportation routines
Artificial Intelligence
Detection of abnormal behaviors using a mixture of Von Mises distributions
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Propagation networks for recognition of partially ordered sequential action
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A Comparison of the Stability Characteristics of Some Graph Theoretic Clustering Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
A universal finite memory source
IEEE Transactions on Information Theory
An unsupervised approach to activity recognition and segmentation based on object-use fingerprints
Data & Knowledge Engineering
What is happening now? Detection of activities of daily living from simple visual features
Personal and Ubiquitous Computing
Relational Graph Mining for Learning Events from Video
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Recognizing multi-user activities using wearable sensors in a smart home
Pervasive and Mobile Computing
Leveraging human behavior models to predict paths in indoor environments
Pervasive and Mobile Computing
Unsupervised discovery, modeling, and analysis of long term activities
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
Human activity monitoring by local and global finite state machines
Expert Systems with Applications: An International Journal
Recognizing water-based activities in the home through infrastructure-mediated sensing
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Modeling complex temporal composition of actionlets for activity prediction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Detecting insider threats in a real corporate database of computer usage activity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Multimedia Tools and Applications
Hi-index | 0.00 |
Formalizing computational models for everyday human activities remains an open challenge. Many previous approaches towards this end assume prior knowledge about the structure of activities, using which explicitly defined models are learned in a completely supervised manner. For a majority of everyday environments however, the structure of the in situ activities is generally not known a priori. In this paper we investigate knowledge representations and manipulation techniques that facilitate learning of human activities in a minimally supervised manner. The key contribution of this work is the idea that global structural information of human activities can be encoded using a subset of their local event subsequences, and that this encoding is sufficient for activity-class discovery and classification. In particular, we investigate modeling activity sequences in terms of their constituent subsequences that we call event n-grams. Exploiting this representation, we propose a computational framework to automatically discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding characterizations of these discovered classes from a holistic as well as a by-parts perspective. Using such characterizations, we present a method to classify a new activity to one of the discovered activity-classes, and to automatically detect whether it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our approach in a variety of everyday environments.