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Hi @kcg2015,
Thanks for your great work.
I have a question on function Deal with matched detections in main.py.
Assume in the 1st frame, there is no matched detections, then we will jump to if len(unmatched_dets) > 0:. It does the tmp_trk.predict_only() by using current detection (x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T; tmp_trk.x_state = x).
In the 2nd frame, I assume that all objects in 1st frame are matched to detections in 2nd frame, then we jump to if matched.size > 0, and do predict. However, in tmp_trk.kalman_filter(z), I saw that the code does predict then update with current detection info. I am confused because we already did predict when processing detection in the 1st frame?
Can you clarify my confusion?
I suppose that we should do update and then predict.
Thank you so much.
The text was updated successfully, but these errors were encountered:
Hi @tringn,
I agree with what you supposed. In the matched detection and tracker, I also think it should be update first followed by predict.
Hi @kcg2015,
Thanks for sharing you work and could you please clarify this if we are wrong?
Hi @kcg2015,
Thanks for your great work.
I have a question on function
Deal with matched detections
inmain.py
.Assume in the 1st frame, there is no matched detections, then we will jump to
if len(unmatched_dets) > 0:
. It does thetmp_trk.predict_only()
by using current detection (x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T; tmp_trk.x_state = x
).In the 2nd frame, I assume that all objects in 1st frame are matched to detections in 2nd frame, then we jump to
if matched.size > 0
, and do predict. However, intmp_trk.kalman_filter(z)
, I saw that the code does predict then update with current detection info. I am confused because we already did predict when processing detection in the 1st frame?Can you clarify my confusion?
I suppose that we should do update and then predict.
Thank you so much.
The text was updated successfully, but these errors were encountered: