inhwanbae / eigentrajectory Goto Github PK

Thanks for your work

You propose to use SVD to construct ET space for normalized trajectory A(L*N). I'm not quite sure if N here represents the number of all tracks in each training set ("eth" "hotel" "univ" "zara1" "zara2")?

Thank you for sharing the great prediction method. When is the code going to be available?

Thank you for your work!

When I was downloading the pretrained model file, I found that different datasets have a corresponding "static.dist" coefficient, such as the ETH dataset's "static.dist": 0.419, the ZARA1 dataset's "static.dist": 0.338, and so on. Now I want to use the SDD dataset for training. How should I determine the corresponding "static_dist"? How much is it confirmed?

Looking forward to your reply！

Could you please provide the download link of the SDD dataset you used in the paper?

Thanks for your work.

The method has some anchors of the coefficients. You use the minimal difference between the reconstruction and ground-truth trajectory to choose the right anchor when training. However, during inference, since we don't have the GT trajectory, how to choose the right anchor and do reconstrution?

Thanks for your amazing work, I am wondering that where can I download the GCS dataset
mentioned in your paper?