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Simple Tracking of 3D Rigid Bodies in Fluids

Simple Tracking of 3D Rigid Bodies in Fluids. Harper Langston. Motivation. Ability to track objects moving in fluids may provide insight into properties of the fluid Verify numerical simulations Study aquatic dynamics (current work in Math Department for example)

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Simple Tracking of 3D Rigid Bodies in Fluids

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  1. Simple Tracking of 3D RigidBodies in Fluids Harper Langston

  2. Motivation • Ability to track objects moving in fluids may provide insight into properties of the fluid • Verify numerical simulations • Study aquatic dynamics (current work in Math Department for example) • Create nice animations, recreate scene with moving camera in 3D, etc.

  3. Simple Setup • Two cameras with sloppy calibration (front view/side view) • One cheap fish tank bought at Petco • Multi-colored balls, sliced open to allow desirable amount of water in and a cardboard ramp

  4. First Experiments • Track a single ball, using Lucas-Kanade tracker and see how it looks • Pretty good as can be seen from video (slowed down 10 times on bottom – good way of faking higher viscosity or heavier object)

  5. Getting More Complicated • With the single ball, there was very little movement in the ‘x’-direction of camera #2, or in the global ‘z’-direction. Once we add the ‘ramp’, it gets more complicated • Sloppy calibration: Use 1-in marks on side of fish tank as well as knowledge of camera location and size of tank. Further, measure radii of spheres in both camera views to find a scale factor. Choose camera #1 as ‘true’ values, and scale camera #2’s measurements accordingly.

  6. Multiple Objects Continued • Simply using Lucas-Kanade doesn’t cut it. It would be good to use Condensation, but let’s see if we can get away without it. • To distinguish colored items, use ‘Skin-Color Tracker’, computing HSV to isolate the layer for each color.

  7. Continuing • Now, it is easier to use Lucas-Kanade iteratively to track each object. Of course there is some degradation and noise, so use a Kalman filter to clean it up! May takes time for each motion to find right parameters but is smooth.

  8. Let’s put it together • First, view the animation I captured from the front

  9. Result • With Skin-Color Tracker, Lucas-Kanade and Kalman Filter (Without purple ball)

  10. How the Animation is Produced • Treat each ball as a single-jointed object and create a biovision format file (http://www.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/BVH.html) • Allows us to consider extensions to multi-jointed dynamic setups possibly • The Bvh file is imported into Maya using Maya’s MEL scripting language to create key frames for the ‘skeleton’ • Physical geometric object is created in Maya and bound to the one-jointed skeleton • Water effects and lighting, etc.!

  11. Continuing On • Spent time working on adding Lucas Kanade with rotations [Bergen et al. 1992, “Three-Frame Algorithm for Estimating Two-Component Image Motion”]. Like we discussed in class, this allows us to find any affine transformation

  12. Is Lucas-Kanade with Rotations, Scaling and Shearing Worth It • Clearly, the last animation had many obstructions. Putting aside the clear need for a less crude setup, the Condensation algorithm would be a better choice for scenes cluttered with potentially many ‘particles’ • Extension could also include [Koller-Meier, Ade, ‘Tracking Multiple Objects Using the Condensation Algorithm”]

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