Comments (4)
Could we use transitions for more efficiently move the prediction location?
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I've done some profiling with Google Chrome's profiler. Most of the bottlenecks where in clmtrakr as expected. One potential area of optimization is the LUDecomposition in the mat.js file as that was taking up a significant fraction of the execution time.
from webgazer.
Here are the three prime spots for optimization that I have identified:
- The ridge() method and it's submethod is probably our best bet for improved performance.
- track() inside of CLMR tracker
- getPupilFeatures() and it's methods in CLMGaze is also in need of potential optimization.
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Performance improved in a7962dc
At this point, we're at a local limit of what we think we can improve performance-wise. Please open a new issue with specific ideas for improving the performance.
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Related Issues (20)
- It does not catch when user closed their eyes! it should make the border red when eyes are closed.
- Webgazer stop collecting data on tab change
- Negative viewport coordinates HOT 1
- Failed to fetch saved models in example HOT 4
- showVideo() causing loss of tracking when switched repeatedly on Firefox v116 HOT 3
- How to use removeMouseEventListeners HOT 2
- a question/help needed! HOT 1
- Error caused by not importing files with the ".mjs" extension
- Defining gaze prediction rate? HOT 1
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- `getStoredPoints` returning empty lists HOT 3
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- Webgazer runs laggy on Android native code HOT 1
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- Error fetching tensorflow models HOT 4
- Unexpected Fetch TypeError in WebGazer HOT 3
- webgazer not work
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