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Would you like to share the GT? #1
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I have found the corresponding OTB groundtruth, and I still have an inquiry. What is the main difference after using the MMR in this work, comparing to the original work proposed by Dr. Yang? (http://users.umiacs.umd.edu/~fyang/papers/wacv14.pdf) Could you go over it more specifically? Thank you! |
Hello HaFred, Hoping this would help you in your work. |
Thank you for your reply! You have really done a good job on this project! I still have 2 questions, hopefully, you could reply.
Really looking forward to your reply! Appreciated it! |
For your first question I left the the multiple layer part because it was making the tracking slow and also not much helping in improving the results. It might be beneficial for multi-object tracking but that I have to check. For your second question can you please elaborate it a little more, if you can provide a small example it will help me in understanding your problem. |
Thank you! In this case, actually, it is a gas component analysis problem. There is a training dataSet X[NUM× CHANNEL], every row denotes a certain gas sample. and every column can be considered as a feature. The label is Y[NUM×3], for each column it denotes the concentration of a certain gas. Say for X_i, the corresponding Y_i=[0.31 0.23 0.54] indicates that sample i contains 0.31 units of CO2, 0.23 units of METHANOL, 0.54 units of ACETONE. So my question is, is it feasible to infer a certain label,'Y_test' with a new 'X_test' as input using MMR? Thank you very much! |
Yes, you can definitely use MMR for this problem in my opinion. MMR can really help you in solving this problem. |
Mr. Nayeem, thank you for your help! I am going through your whole project and grasping the whole idea of how the MMR updates the S and A repectively. I have to say, your implementation combining dictionary_init and S&A updating is really amazing! Hope that I can learn how to do MMR specifically with reviewing your project. Thank you very much! |
There is one more puzzle left... I spent hours thinking and debugging but I cannot make it clear. Why is that only the very first row of the groundtruth file (for OTB_football dataSet, its dimension is [362×4]), is being used? That is to say, only the groundtruth of the first frame is used in the classifying part. You used your 2-dimensional label as new groundtruth to denote if the frame belongs to background or not, so what is the relationship between your new groundtruth and the given groundtruth? Because I don't see any usage except for the first row like I mentioned above. But MMR supposed to be supervised regression right? How can we set the groundtruth(label) aside? Thank you! |
You are absolutely correct, I am only using the groundtruth bounding box for the first frame only because I need to initialize my tracker. The tracking approach I am using need to know the foreground location for the very first frame. So I am taking the gt bounding box location for first frame as foreground and creating random boxes around it of varying sizes as background and then learning the S and A. And then after every 5th frame S and A is again updated by all the saved features and label saved during tracking those 5 frames. |
I got it. Thank you for your explanation! |
Hi, I ran the codes and an error occurred. The 'Groundtruth_rect' is missing. I am wondering where could I acquire it?
THANK YOU!
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