Difference between revisions of "Tracker"

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(Added to Software category)
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==Tracker Program Description==
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==Tracker Program Description and Tutorial Video==
  
Tracker is a working name for software to track the path of animal movements in experiments.
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Tracker is a working name for software to track the path of animal movements in experiments. Click on the screenshot to see a brief tutorial on how to use Tracker.
[[image:TrackerScreenShot.jpg|700px]]
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[[image:TrackerScreenShot.jpg|700px|link={{SERVER}}/calsnap/Tracker-Tutorial.mp4]]
  
 
==Source Code and Auto Build==
 
==Source Code and Auto Build==

Revision as of 18:05, 13 January 2010

Tracker Program Description and Tutorial Video

Tracker is a working name for software to track the path of animal movements in experiments. Click on the screenshot to see a brief tutorial on how to use Tracker.

TrackerScreenShot.jpg

Source Code and Auto Build

Tracker Project Code

Tracker Auto Build

Proposed Functionality

Input:

  1. Quicktime movies of experiment

Outputs:

  1. Numeric output
    1. Instantaneous
      1. time
      2. position
      3. orientation
    2. Derived
      1. velocity
      2. distance
      3. direction of motion
      4. measure of wall taxis
  1. Generated Movies
    1. Extracted data graphic.
    2. Overlay of extracted data graphic and original video.


Proposed Implementation

Time determined from Quicktime movie time stamps.

Position is average position of blob pixels.

Orientation is average vector from average position to blob pixels. Position and orientation can be used to define oval around body.

Direction of motion, velocity and distance are calculated from Time, Position and Orientation in the usual way.


TrackerAngles.jpg

Proposed Technology

Cocoa application.

Use QuickTime filter to convert video to 2-color B&W via brightness cutoff.

Filter (either QuickTime or after frame extraction) to clip non-experiment portion of video.

Frame-by-frame code to detect blob and calculate position, etc.

Alternative Implementation

Use grayscale instead of B&W. Position, etc would be calculated based on weighted pixels. This would be able to pick up sub-pixel changes and provide smoother, more accurate data.