Abstract

Based on the Lagrangian framework for fluid dynamics, a streakline representation of flow is presented to solve computer vision prob- lems 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 par- ticles at each frame, then moving both current and past particles using op- tical flow. Streaklines are the locus of points that connect particles which originated from the same initial position. In this paper, a streakline tech- nique 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 tech- niques, showing that the streakline method outperforms the state-of-the- art in segmentation, and opening a new domain of application for crowd analysis based on potentials.