Moving Vistas: Exploiting Motion for Describing Scenes
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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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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.