Comments (1)
adipandas
commented on August 24, 2024
Hello @ashutoshdhanda,
I am glad you found this work useful.
I have not investigated any hyperparameter tuning packages while working on this project. I won't be able to advise on how to go about it. I would suggest you follow the tutorials for the respective packages.
For tracking purposes, the main parameters to tune would be the motion model covariance matrices of Kalman Filters, IoU thresholds and object lost count after its first detection. I believe depending on what objects are being tracked and their speeds, these parameters would vary. So for each situation, one would need to run the tuning experiment separately.
Another approach would be to use visual information in tracking to avoid such tuning. I wish to include this but haven't found enough time to complete that part.
Thank you.
from multi-object-tracker.
Related Issues (20)
- How did you choose the Process & Measurement noise? HOT 1
- In the SORT tracker, IOU threshold is not working. HOT 2
- what is max_lost? HOT 1
- Do you have any plan to add V-IOU Tracker? HOT 1
- AttributeError: 'SORT' object has no attribute 'track' HOT 1
- Is there any tracker prediction? HOT 2
- How to maintain the same ID for a detected object across many frames? HOT 1
- x,y,z values alvays gives -1,-1,-1 HOT 1
- Getting the exact same result when changing tracker type HOT 1
- Bug in detector_YOLOv3.py
- Adding Deepsort Tracker HOT 1
- Project dependencies may have API risk issues HOT 1
- Starting example is not runnable (uses track method instead of update) HOT 1
- PyPI Package HOT 8
- Plz add Requirements HOT 1
- class_ids is not considered in the tracking HOT 1
- To predict a detection without a detection by a detector HOT 1
- Bug: SORT always same iou_threshold HOT 1
- Fix cv2 version checking in motrackers.detectors.yolo.py HOT 2
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