Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Face Recognition Based on Discriminative Manifold Learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Analyzing Human Movements from Silhouettes Using Manifold Learning
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Video event segmentation and visualisation in non-linear subspace
Pattern Recognition Letters
Probabilistic expression analysis on manifolds
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition
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
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This paper addresses the problem of analyzing video events in crowded scenes. A novel manifold learning method is proposed to achieve visualization and modeling of video events in a low dimensional space. In the proposed approach, a video is considered as a trajectory of frames in a low-dimensional space. This low-dimensional representation of a video preserves the spatio-temporal property of a video as well as the characteristic of the video. Different tasks of video content analysis such as visualization, video event segmentation and abnormality detection are achieved by analyzing these video trajectories based on the Hausdorff distance similarity measure. We evaluate our proposed method on the state-of-the-art public data-sets containing different crowd events. Qualitative and quantitative results show the promising performance of the proposed method.