Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
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
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Fuzzy qualitative human motion analysis
IEEE Transactions on Fuzzy Systems
Activity Modeling Using Event Probability Sequences
IEEE Transactions on Image Processing
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Motion trajectories provide rich spatio-temporal information about an object activity. In this paper, we present a novel anomaly detection framework to detect anomalous motion trajectory using the fusion of adaptive piecewise analysis and fuzzy rule-based method. That is, first of all we address the problem by segmenting our moving objects using a Gaussian mixture background model. Secondly, visual tracking using probabilistic appearance manifolds to extract spatio-temporal trajectory. Thirdly, adaptive piecewise analysis and data quantization are performed on the extracted trajectory such that the anomalous detection can be performed as the incoming data are acquired. Finally, through the accumulative rank of the adaptive piecewise analysis and a fuzzy rule-based anomaly detection framework to detect the anomalous trajectory. Experimental results on various challenging trajectory data has validated the effectiveness of the proposed method.