Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
One-class svms for document classification
The Journal of Machine Learning Research
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
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
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Unusual Event Detection via Multi-camera Video Mining
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
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
Activity based surveillance video content modelling
Pattern Recognition
Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Real-time human action recognition by luminance field trajectory analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Video event segmentation and visualisation in non-linear subspace
Pattern Recognition Letters
Geometric Mean for Subspace Selection
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
Infinite Hidden Markov Models for Unusual-Event Detection in Video
IEEE Transactions on Image Processing
Video manifold modelling: finding the right parameter settings for anomaly detection
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Abnormal event detection in crowded scenes using sparse representation
Pattern Recognition
An analytical framework for event mining in video data
Artificial Intelligence Review
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We present a computational approach to abnormal visual event detection, which is based on exploring and modeling local motion patterns in a non-linear subspace. We use motion vectors extracted over a region of interest (ROI) as features and a non-linear, graph-based manifold learning algorithm coupled with a supervised novelty classifier to label segments of a video sequence. Given a small sample of annotated normal motion vectors, the non-linear detector ranks segments in a sequence as a function of abnormality. We evaluate the proposed method and compare its performance against the use of other low-level features such pixel appearance and change detection masks. Our choice of feature space compares favorably to the alternatives in terms of classification performance, sensitivity to noise as well as computational complexity.