Comments (3)
abewley
commented on July 21, 2024
The box detections from Faster RCNN are saved to file for demonstration purpose. This allows people to run the tracker is without needing to install and integrate Faster RCNN.
If you want to run the detector on a live image stream and feed the detections directly into the tracker then you will need to:
First follow the py-faster-rcnn installation instructions and adapt their demo code to work with your data.
Secondly, feed the detections into the tracker as explained in the README.
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harshmunshi
commented on July 21, 2024
The data essentially is the bbox information right?
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abewley
commented on July 21, 2024
That's correct. The files follow the same format as used for the MOT challenge:
https://motchallenge.net/instructions/
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Related Issues (20)
- running SORT on custom dataset
- missing intermediate IDs HOT 2
- Using SORT when the number of elements to track is constant and known - ideas to recover missidentification and missmatches HOT 6
- C-Python implementation of SORT
- Multi-class multi-object tracking HOT 3
- error in iou__batches HOT 7
- Tracking other objects HOT 2
- Why are FP and FN different for each Tracker method in the paper?
- Preparing RLE for MOTS20 evaluation
- SORT ID for new detection between other old detections
- Logic Error? trk.hit_streak >=self.min_hits, ret.append(...)
- multiclass tracking HOT 3
- can I use this for windows ? because it still show me the same error ?
- No issue, just here for `lap` -> `lapx`
- instalation error
- How to get next state KalmanFilter
- ValueError: operands could not be broadcast together with shapes (0,) (1,3) HOT 1
- Python Version support for SORT
- Converting into Grayscale increasing the inference time ? Is there any way we can increase the model inference time ? HOT 1
- ImportError: Cannot load backend 'TkAgg' which requires the 'tk' interactive framework, as 'headless' is currently running HOT 2
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