Skip to content Skip to navigation

Connexions

You are here: Home » Content » Future Work in CS Motion Detection

Navigation

Content Actions
  • Download module PDF
  • Add to ...
    Add the module to:
    • My Favorites
    • A lens
    • An external social bookmarking service
    • My Favorites (What is 'My Favorites'?)
      'My Favorites' is a special kind of lens which you can use to bookmark modules and collections directly in Connexions. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need a Connexions account to use 'My Favorites'.
    • A lens (What is a lens?)

      Definition of a lens

      Lenses

      A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

      What is in a lens?

      Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

      Who can create a lens?

      Any individual Connexions member, a community, or a respected organization.

    • External bookmarks
  • E-mail the authors

Lenses

What is a lens?

Definition of a lens

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...

Affiliated with (What does "Affiliated with" mean?)

This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • Rice University ELEC 301 Projects

    This module is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301As a part of collection:"ELEC 301 Projects Fall 2005"

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

Recently Viewed

This feature requires Javascript to be enabled.

Future Work in CS Motion Detection

Module by: Siddharth Gupta, Veena Padmanabhan, Grant Lee, Heather Johnston

Summary: Some possible investigations in the field

There are many future directions that we can foresee for intelligent motion analysis on compressed sensing data due to the minimal computation and data storage required.

One major point of future study is the feature extraction algorithms for compressed sensing. Our analysis looked at only a very small part of the many features that we think can be gleaned from the compressed sensing measurements. For example we can look at the acceleration, which could be trivial using the difference in the velocity feature. Our calculation of total intensity can also be fully exploited. Similarly, we believe it is possible to develop algorithms for shape recognition.

When the number of feature calculations availiable increases, a Support Vector Machine (SVM) can be used to analyze the data. The SVM is a prediction algorithm which after being trained with sufficient data can classify new data on the basis of the training. This is very useful when the data is multi-dimensional.

Comments, questions, feedback, criticisms?

Send feedback