Activity Representation in Crowd
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Sparse Representation for Video-Based Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Statistical motion information extraction and representation for semantic video analysis
IEEE Transactions on Circuits and Systems for Video Technology
Modeling music as a dynamic texture
IEEE Transactions on Audio, Speech, and Language Processing
Detecting contextual anomalies of crowd motion in surveillance video
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Gait recognition based on improved dynamic Bayesian networks
Pattern Recognition
Change detection for temporal texture in the Fourier domain
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
A unified approach to segmentation and categorization of dynamic textures
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Pursuing atomic video words by information projection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Joint thrombus and vessel segmentation using dynamic texture likelihoods and shape prior
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Space-time spectral model for object detection in dynamic textured background
Pattern Recognition Letters
Computer Vision and Image Understanding
Learning to segment a video to clips based on scene and camera motion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks
Expert Systems with Applications: An International Journal
Dynamic texture segmentation based on deterministic partially self-avoiding walks
Computer Vision and Image Understanding
Abnormal crowd behavior detection and localization using maximum sub-sequence search
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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A dynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures, a statistical model for an ensemble of video sequences that is sampled from a finite collection of visual processes, each of which is a dynamic texture. An expectationmaximization (EM) algorithm is derived for learning the parameters of the model, and the model is related to previous works in linear systems, machine learning, time-series clustering, control theory, and computer vision. Through experimentation, it is shown that the mixture of dynamic textures is a suitable representation for both the appearance and dynamics of a variety of visual processes that have traditionally been challenging for computer vision (e.g. fire, steam, water, vehicle and pedestrian traffic, etc.). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (e.g. optical flow or other localized motion representations), the mixture of dynamic textures achieves superior performance in the problems of clustering and segmenting video of such processes.