Comments (3)
Hi @berkantay,
I am glad you liked my repo!
This is a two stage algorithm: first yolo extract objects of interest, in this case pedestrians; then, this objects are passed to deep sort which tracks them. You could boost the performance both by increasing the accuracy of yolov5 but also by training a better deep learning metric.
You can make the experiment of training Yolov5s, Yolov5m and Yolov5l on your custom dataset and see how the different models affect the inference. The short answer is: Yolov5s < Yolov5m < Yolov5l < Yolov5x
The tracker is trickier as you would need a REID dataset for your objects of interest to train the deep learning metric on.
from boxmot.
Hello @mikel-brostrom I enhanced my training performance. Everything is perfect for detection, But there is one spot that needs to be clarified. Does the tracker needs training for what to track?
from boxmot.
Yes, Deep Sort extends the original SORT to integrate appearance information based on a deep appearance descriptor. Hence the name, DEEP SORT. This appearance descriptor is given by a CNN model presented under Table 1 in the paper DEEP SORT. This model was trained on a large-scale person re-identification dataset that contains over 1,100,000 images of 1,261 pedestrians, making it well suited for deep metric learning in a people tracking context.
Although this metric can be used for other objects that are not people, it may be far from optimal for the specific objects that you want to track. If you want to improve the performance of the tracker you could try to train that deep appearance descriptor on a custom REID dataset containing the objects you want to track
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Related Issues (20)
- How do I keep track in low frame rate video? HOT 1
- How can we calculate the FPS of each tracker? HOT 1
- Run Strong Sort Tracker on GPU HOT 3
- AttributeError: 'BYTETracker' object has no attribute 'reset' HOT 1
- What should I do when there is no detection HOT 5
- train a REID MODEL HOT 2
- How to combine ocsort and other algorithms you provide with yolov8? HOT 2
- StrongSORT plotting results HOT 4
- TensorRT HOT 9
- Applicability of Boxmot in my case HOT 6
- `KalmanBoxTracker.observations` grows infinitely, causing out of memory HOT 1
- Unable to pass `embs` to tarcker HOT 3
- it can't run when i start tracking
- How can i use ZED camera for this approach? HOT 1
- (OCSORT, DeepOCSORT) shape specs not match doc function HOT 3
- Why reid is not working at all in my case? HOT 4
- Disable ReID module HOT 3
- Using different Reid models to evaluate DeepOcsort, but getting the same evaluation results HOT 2
- PyTorch version automatic update HOT 2
- Evaluation using bytetrack
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from boxmot.