Comments (4)
mikel-brostrom
commented on May 24, 2024
I identified the slow part. step2 in bot_sort.py:
Line 291 in 03a3a68
Some example of timed steps:
0: 480x640 1 person, Done. YOLO:(0.045s), BoTSORT:(0.092s)
step2: 0.46398663699999965
step3: 0.0002665390000000656
step4: 6.4769999994496175e-06
step5: 6.332999999969502e-06
merge: 0.000242242999998865120: 480x640 1 person, Done. YOLO:(0.044s), BoTSORT:(0.125s)
step2: 0.511612865
step3: 0.00026188500000046133
step4: 4.029000001182226e-06
step5: 4.534000000333549e-06
merge: 0.000188421000000715820: 480x640 1 person, Done. YOLO:(0.044s), BoTSORT:(0.225s)
step2: 0.21331869099999956
step3: 0.00011200399999999888
step4: 1.0119999984681272e-06
step5: 1.358000000450943e-06
merge: 0.0001030410000009141from bot-sort.
mikel-brostrom
commented on May 24, 2024
Ok, the camera motion compensation seems to be the issue:
# Fix camera motion
warp = self.gmc.apply(img, dets)
STrack.multi_gmc(strack_pool, warp)
STrack.multi_gmc(unconfirmed, warp)from bot-sort.
NirAharon
commented on May 24, 2024
Hi @mikel-brostrom, this is not normal behavior.
Please try to use the new --cmc-method I uploaded: sparseOptFlow.
Please let me know if it was able to fix this problem.
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mikel-brostrom
commented on May 24, 2024
Sorry for my late response. Just saw that I never responded to this. Yes, this solved the real-time issue. Could you provide a rationale why before we leave this behind us 😄?
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