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View Code? Open in Web Editor NEWScalable spatio-temporal clustering method for detection of crowd motion patterns. Implementation in Python 2.7
License: MIT License
Scalable spatio-temporal clustering method for detection of crowd motion patterns. Implementation in Python 2.7
License: MIT License
Hello, first of all thank you for sharing your work!
I was testing and was finding it was really sensitive to the parameters (and that ones would influence each other's choice severely). So I start looking at the code together with the paper and I found something different: the normalization
You seem to normalize the tracklets (both position and velocity) with:
normalize = alpha * 2 + beta * 2
then passing these normalized tracklets to prepare_quick_shift.
I experimented by doing the normalization individually for position and velocity inside this last function
dist_p = np.linalg.norm(p[i] - p[neighbors], axis=1) / alpha
dist_v = np.linalg.norm(v[i] - v[neighbors], axis=1) / beta
And found it to be much more robust, even when changing scenarios.
I'd like to know why you did such normalization, maybe I'm missing something... Thank you again!
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