Closed learnuser1 closed 11 months ago
Hi, I face a similar problem with a video of one person running seen from the side. If left leg is in the front, points on the right leg stay still after they have been occluded by left leg. All points stay still if they are occluded by a vertical stick for a short time
Hi @learnuser1, @fenaux,
Could you send me an example of such a video with estimated tracks? How many points are you tracking and what grid_size
/ local_grid_size
do you use?
Also, have you tried other CoTracker configurations, such as stride_4_wind_12
?
In my experience, tracking is always better with a non-zero grid_size
because the model can identify similarly moving points and pay attention to background points to compensate for camera motion. A grid_size
of 4/5/6 works best for a small number of points (less than 50). When tracking a handful of points, local_grid_size
can also improve tracking performance.
Thanks, I will try local_grid_size anyway here are two sample. Queries are the keypoints as obtained by a top down human pose
https://github.com/facebookresearch/co-tracker/assets/44709416/840206c7-ff44-430b-a11b-02d287097cd2
https://github.com/facebookresearch/co-tracker/assets/44709416/05182e99-e46e-4c68-b03b-670d12a016ae
Thanks @fenaux, could you also send the original video with queried points? I'll try to make it work better
@nikitakaraevv Many thanks for the interest you deserve to my questions Here is the video (I do not know if it uploaded correctly !)
https://github.com/facebookresearch/co-tracker/assets/44709416/a0a81cdb-9a8d-4a4a-98be-b0cb8af3a2e4
queries from frame number 2 tensor([[ 2., 127., 124.], [ 2., 127., 124.], [ 2., 96., 141.], [ 2., 156., 163.], [ 2., 86., 180.], [ 2., 195., 167.], [ 2., 66., 186.], [ 2., 219., 179.], [ 2., 87., 185.], [ 2., 199., 164.]])
queries from last frame tensor([[229., 99., 112.], [229., 99., 112.], [229., 57., 122.], [229., 120., 154.], [229., 51., 172.], [229., 151., 156.], [229., 35., 186.], [229., 163., 158.], [229., 54., 164.], [229., 158., 174.]])
@nikitakaraevv here is a case where your method works nicely :1st_place_medal: Each runner is initialized with a local grid [10,5] Note that one corridor 9 runner is lost just at finish lane. Conventional trackers loose runners in corridor 5 and 6 when they pass the olympic rings
https://github.com/facebookresearch/co-tracker/assets/44709416/17e9798a-72bf-4741-b589-1bad8281866c
Hi @fenaux, thank you for sending me these videos! I've tried different things but unfortunately wasn't able to significantly improve the result with simple tricks. I'll keep working on CoTracker, so stay tuned! If you find other examples where the model doesn't work well, please let me know! I'm closing the issue for now.
@fenaux I'm working on a similar problem, to track athletes' body joints and use the tracks for motion kinematics analysis. Can I hit you up and discuss more about it?
@zhuolisam yes provide me a mail for instance
@fenaux here this is my email zhuolisam0627@gmail.com
Hello, when I’m using a specific number of points to track in a video for autonomous driving inference, I’ve noticed that once the target object is occluded, the tracked points start following other objects, resulting in poor tracking performance. Are there any good methods to address this issue of poor performance after occlusion? Which approach, using points or a grid, yields better results in tracking when facing occlusion?