Comments (5)
I don't believe so - if you change the parameters you will likely have to
retrain the tracker.
On Sat, Oct 8, 2016 at 10:32 PM, raoqi [email protected] wrote:
I am now learning this method to do other tasks and I find it not ideal,
so that I modify the parameters in 'helper/bouding_box.cpp' including '
kContextFactor ' and 'kScaleFactor ' to let the tracker search in a
smaller region.
But I found the output not ideal, all the bouding boxes turn out to be a
small red dot finally.My question is: is it exist a way to modify the parameters of 'sizeof
search region' in order to let the tracker do other task(here I use it to
track the dead object in background in order to calculate background
optical flow so that I want to modify parameters to let it search in a
smaller region)without re-train the tracker?β
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Ok, I know.
And do you remember how long have you spent to train your tracker?
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A few days, I believe?
On Mon, Oct 10, 2016 at 1:29 PM, raoqi [email protected] wrote:
Ok, I know.
And do you remember how long have you spent to train your tracker?β
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@davheld Hi, @rao1219 I want to increase the searching area during the test using VOT2014, do I need to retrain the model or just change the parameters in 'helper/bouding_box.cpp' including 'kContextFactor ' and 'kScaleFactor ' ?
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In theory, I don't think you should need to retrain the model, but in practice I found that it helps a lot if you do. You can try it without retraining, but I'm guessing that you'll need to retrain. If anyone else has tried this, feel free to pipe in here.
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Related Issues (20)
- Make error HOT 1
- Attempting to use an MPI routine before initializing MPI HOT 1
- Segmentation fault HOT 3
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- The Annotation cannot be loaded
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- integrate GOTURN in the VOT toolkit
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- Train on Images Only HOT 1
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- The link to pretrained tracker model is invalid. HOT 2
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