Comments (2)
abewley
commented on July 21, 2024
This approach is so simple that you do not need to "pretrain" any model. Everything you need is in the code.
Note that this code focuses on the task of tracking not detection so you will need detections from your favourite detector. I've supplied some for the MOT benchmark based on FasterRCNN.
If you are after a learnt tracker then please see the DeepSORT repo
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Ironeie
commented on July 21, 2024
Thank you so much
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Related Issues (20)
- from sort import * causes dead Kenel HOT 1
- 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
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