The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Estimating the Support of a High-Dimensional Distribution
Neural Computation
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental and adaptive abnormal behaviour detection
Computer Vision and Image Understanding
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Online prediction of time series data with kernels
IEEE Transactions on Signal Processing
Real-Time Abnormal Event Detection in Complicated Scenes
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Abnormality detection using low-level co-occurring events
Pattern Recognition Letters
Anomalous video event detection using spatiotemporal context
Computer Vision and Image Understanding
Human behavior clustering for anomaly detection
Frontiers of Computer Science in China
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Sparse reconstruction cost for abnormal event detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
Online Kernel Principal Component Analysis: A Reduced-Order Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vectormachine (SVM) firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique.