Closed AtaAtasoy closed 2 months ago
Hi @AtaAtasoy, thanks for your question!
Given a query point x_q
and a tracked prediction x_t
, there are several signals that can indicate the prediction confidence, most of which you already mentioned:
x_q
and x_t
x_q
and x_t
. median_dists_from_source_anchor_vis
can serve for this, but you may also consider all of the frames, and not just the anchor framesx_q
to the features in frame t
, i.e. how confidently does the refined feature of x_q
match to the tracked feature x_t
. Let me know if you'll have further questions.
@AtaAtasoy I'm closing this issue for now. If you'll have further questions, feel free to open it again.
Hi, first of all, congrats on your great work!
As far as I know, the visibility/occlusion value is inferred discretely (occluded or not) in model_inference.py with:
Is there a way to calculate some "confidence" value used in some trackers or detectors? The goal would be to use it as a weight for the tracker's trajectory prediction.
Would it make sense to use a value such as
median_dists_from_source_anchor_vis * traj_cos_sim_qp
(if not occluded) or perhapsocclusion * cos_sims
where cos_sims is the result ofcompute_trajectory_cos_sims
in model_inference.py?I would appreciate any guidance regarding this issue. Thank you for your time.