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Avalanche

Moving Vistas: Exploiting Motion for Describing Scenes. Nitesh Shroff, Pavan Turaga, Rama Chellappa University of Maryland, College Park. Problem Definition and Motivation. Dynamic Scene Dataset. Dynamic Attributes. Ski-Resort. Dynamic Scene Recognition

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  1. Moving Vistas: Exploiting Motion for Describing Scenes Nitesh Shroff, Pavan Turaga, Rama Chellappa University of Maryland, College Park Problem Definition and Motivation Dynamic Scene Dataset Dynamic Attributes Ski-Resort • Dynamic Scene Recognition • Dynamics of scene reveals further information !! • Motion of scene elements improve or deteriorate classification? • How to expand the scope of scene classification to videos? • Unconstrained YouTube videos • Large Intra-class variation • Available at http://www.umiacs.umd.edu/users/nshroff/DynamicScene.html Linear Separation using Attributes • Degree of Busyness: Amount of activity in the video. • Highly busy: Sea-waves or Traffic scene --high degree of detailed motion patterns. • Low busyness: Waterfall -- largely unchanging and motion typically in a small portion • Degree of Flow Granularity of the structural elements that undergo motion. • Coarse: falling rocks in a landslide . • Fine: waves in an ocean • Degree of Regularity of motion of structural elements. • Irregular or random motion: chaotic traffic • Regular motion: smooth traffic Avalanche Snow-Clad Mountain Each dimension as time series Whirlpool Waves Classification & Learn Attributes GIST [1] for each frame Chaotic Invariants 18 out of 20 correctly classified Degree of Busyness Degree of Regularity Contributions • Dynamic Attributes • motion information from a global perspective. • Characterize the unconstrained dynamics of scenes using Chaotic Invariants. • Does not require localization or tracking of scene elements. • Unconstrained real world Dynamic Scene dataset. Regularity Chaotic Invariants[2,4] Busyness • Requires No assumptions • Purely from the sequence of observations. • Fundamental notion -- all variables in a influence one another. • Constructs state variables from given time series • Estimate embedding dimension and delay • Reconstruct the phase space. • Characterize it using invariants • Lyapunov Exponent: Rate of separation of nearby trajectories. • Correlation Integral: Density of phase space. • Correlation Dimension: Change in the density of phase space Recognition Accuracy Algorithmic Layout Modeling Dynamics • What makes it difficult? • Scenes are unconstrained and ‘in-the-wild’ -- Large variation in scale, view, illumination, background • Underlying physics of motion -- too complicated or very little is understood of them. • Ray of hope !!! • Underlying process not entirely random but has deterministic component • Can we characterize motion at a global level ?? • Yes using dynamic attributes and chaotic invariants References • [1] A.Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 2001 • [2] M. Perc. The dynamics of human gait. European journal of physics, 26(3):525–534, 2005 • [3] G. Doretto, A. Chiuso, Y. Wu, and S. Soatto. Dynamic textures, IJCV, 2003 • [4]S. Ali, A. Basharat, and M. Shah. Chaotic Invariants for Human Action Recognition. ICCV, 2007.

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