Comments (4)
Can we close this issue?
from tracking_wo_bnw.
I just tested Tracktor++ on the CVPR 2019 test set and it took 1667 seconds to finish all 4 sequences. I updated the MOTChallenge webpage accordingly.
from tracking_wo_bnw.
thanks for the update!
My primary interest is the speed of the performance.
If possible, could you provide some insights on how to improve the speed? Or which module (such as re-id) that can be further optimized in order to gain a further speed-up?
Also, if the number of detected object (peoples) is reduced (say only detect major characters in an image by setting a higher detector threshold ), will this improve the process speed?
from tracking_wo_bnw.
We did not evaluate the runtime of the different steps in our Tracktor algorithm. However, there are multiple things one could do to speed it up. Our object detector is called multiple times. I think the number of calls per frame could be reduced. Running an object detector with a different backbone, e.g., ResNet34 instead of ResNet101, is a possibility if top tracking performance is not essential.
Changing the trajectory killing or bounding box initialisation thresholds should change the number of detected objects but I think the potential for reducing the runtime is comparatively small.
But again, this is something that has to be evaluated to be really sure.
from tracking_wo_bnw.
Related Issues (20)
- Question: reproduce the model HOT 2
- How do you train Tracktor itself? HOT 2
- No module named 'torchreid' HOT 6
- What does the various column items in det.txt file represent? HOT 9
- Should I train det and reid for tracktor? HOT 2
- can
- can't find the module named "tracktor.config" HOT 3
- Looking for more training details of given models HOT 1
- No such file or directory: ...../model_epoch_27.model' HOT 5
- Train reid and detector for custom objects /labels ( not people/cars) HOT 6
- how to reproduce the results of this repository on colab HOT 2
- Online or Offline method HOT 2
- requirments issue HOT 1
- About put the own dataset detection result to do tracker
- RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument tensors in method wrapper___cat) HOT 1
- How to train your own dataset? HOT 1
- Is box regression method predict_boxes applicable to tracking multiple objects, number of interested objects>2
- Issues in requirements.txt
- I cannot open the link of Train and test object detector (Faster R-CNN with FPN) on Google Colab notebook HOT 4
- ModuleNotFoundError: No module named 'mask_rcnn_tracktor' HOT 3
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from tracking_wo_bnw.