Image based flow visualization
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Visualizing Vector Field Topology in Fluid Flows
IEEE Computer Graphics and Applications
Extraction of Singular Points from Dense Motion Fields: An Analytic Approach
Journal of Mathematical Imaging and Vision
Unsupervised Bayesian Detection of Independent Motion in Crowds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Particle Video: Long-Range Motion Estimation using Point Trajectories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Computer Animation and Virtual Worlds - CASA 2007
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
Tracking using motion patterns for very crowded scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Fast regularization of matrix-valued images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Weighted interaction force estimation for abnormality detection in crowd scenes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Vector field analysis for multi-object behavior modeling
Image and Vision Computing
Sparse representation for robust abnormality detection in crowded scenes
Pattern Recognition
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Based on the Lagrangian framework for fluid dynamics, a streakline representation of flowis presented to solve computer vision problems involving crowd and traffic flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position. In this paper, a streakline technique is developed to compute several important aspects of a scene, such as flow and potential functions using the Helmholtz decomposition theorem. This leads to a representation of the flow that more accurately recognizes spatial and temporal changes in the scene, compared with other commonly used flow representations. Applications of the technique to segmentation and behavior analysis provide comparison to previously employed techniques, showing that the streakline method outperforms the state-of-theart in segmentation, and opening a new domain of application for crowd analysis based on potentials.