Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Formulation for Hausdorff Matching
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Video Behaviour Profiling and Abnormality Detection without Manual Labelling
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-scale and real-time non-parametric approach for anomaly detection and localization
Computer Vision and Image Understanding
CYKLS: detect pedestrian's dart focusing on an appearance change
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Situation Awareness in Applications of Ambient Assisted Living for Cognitive Impaired People
Mobile Networks and Applications
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A self-adaptive Hidden Markov Model (SA-HMM) based framework is proposed for behavior recognition in this paper. In this model, if an unknown sequence cannot be classified into any trained HMMs, a new HMM will be generated and trained, where online training is applied on SA-HMMs to dynamically generate the high-level description of behaviors. The SA-HMMs based framework consists of training and classification stages. During the training stage, the state transition and output probabilities of HMMs can be optimized through the Gaussian Mixture Models (GMMs) so the generated symbols can match the observed image features within a specific behavior class. On the classification stage, the probability with which a particular HMM generates the test symbol sequence will be calculated, which is proportional to the likelihood.