Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
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
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
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Unsupervised analysis of activity sequences using event-motifs
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
Appearance-based video clustering in 2D locality preserving projection subspace
Proceedings of the 6th ACM international conference on Image and video retrieval
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Visualization and clustering of crowd video content in MPCA subspace
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning video manifold for segmenting crowd events and abnormality detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
The retrieval of motion event by associations of temporal frequent pattern growth
Future Generation Computer Systems
Video manifold modelling: finding the right parameter settings for anomaly detection
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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We introduce the use of dimensionality reduction for video event detection without explicitly using motion estimation or object tracking. Raw data from video sequences are used to construct a low-dimensional mapping representing the input frames. We compare principal component analysis, multi-dimensional scaling, isomap, maximum variance unfolding and Laplacian eigenmaps and implement an approach based on local, non-linear dimensionality reduction. We propose an approach with a graph based on the similarity of frames and enriched with the temporal information from the sequence processed by Laplacian eigenmaps. This makes it possible to visualise the manifold of motion in the scene and to detect unusual events in a low-dimensional space. We demonstrate the approach on standard traffic surveillance test sequences.