A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Rare Events in Video Using Semantic Primitives with HMM
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
Rule + Exception Strategies for Security Information Analysis
IEEE Intelligent Systems
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Beam sampling for the infinite hidden Markov model
Proceedings of the 25th international conference on Machine learning
Sensor-Based Abnormal Human-Activity Detection
IEEE Transactions on Knowledge and Data Engineering
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Hidden Markov models for multiaspect target classification
IEEE Transactions on Signal Processing
Infinite Hidden Markov Models for Unusual-Event Detection in Video
IEEE Transactions on Image Processing
Cross-domain activity recognition via transfer learning
Pervasive and Mobile Computing
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Computer Vision and Image Understanding
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Detecting abnormal event from video sequences is an important problem in computer vision and pattern recognition and a large number of algorithms have been devised to tackle this problem. Previous state-based approaches all suffer from the problem of deciding the appropriate number of states and it is often difficult to do so except using a trial-and-error approach, which may be infeasible in real-world applications. Yet in this paper, we have proposed a more accurate and flexible algorithm for abnormal event detection from video sequences. Our three-phase approach first builds a set of weak classifiers using Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), and then proposes an ensemble learning algorithm to filter out abnormal events. In the final phase, we will derive abnormal activity models from the normal activity model to reduce the FP (False Positive) rate in an unsupervised manner. The main advantage of our algorithm over previous ones is to naturally capture the underlying feature in abnormal event detection via HDP-HMM. Experimental results on a real-world video sequence dataset have shown the effectiveness of our algorithm.