Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Sensor-Based Abnormal Human-Activity Detection
IEEE Transactions on Knowledge and Data Engineering
Identifying terrorist activity with AI plan recognition technology
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Cross-domain activity recognition
Proceedings of the 11th international conference on Ubiquitous computing
Three challenges in data mining
Frontiers of Computer Science in China
Cross-domain activity recognition via transfer learning
Pervasive and Mobile Computing
Bayesian nonparametric modeling of user activities
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
GPARS: a general-purpose activity recognition system
Applied Intelligence
Accounting for data dependencies within a hierarchical dirichlet process mixture model
Proceedings of the 20th ACM international conference on Information and knowledge management
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Design of a situation-aware system for abnormal activity detection of elderly people
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Computer Vision and Image Understanding
Constructing the Web of Events from raw data in the Web of Things
Mobile Information Systems - Internet of Things
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Detecting abnormal activities from sensor readings is an important research problem in activity recognition. A number of different algorithms have been proposed in the past to tackle this problem. Many of the previous state-based approaches suffer from the problem of failing to decide the appropriate number of states, which are difficult to find through a trial-and-error approach, in real-world applications. In this paper, we propose an accurate and flexible framework for abnormal activity recognition from sensor readings that involves less human tuning of model parameters. Our approach first applies a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), which supports an infinite number of states, to automatically find an appropriate number of states. We incorporate a Fisher Kernel into the One-Class Support Vector Machine (OCSVM) model to filter out the activities that are likely to be normal. Finally, we derive an abnormal activity model from the normal activity models to reduce false positive rate in an unsupervised manner. Our main contribution is that our proposed HDP-HMM models can decide the appropriate number of states automatically, and that by incorporating a Fisher Kernel into the OCSVM model, we can combine the advantages from generative model and discriminative model. We demonstrate the effectiveness of our approach by using several real-world datasets to test our algorithm's performance.