latest/how_to/track_objects/

[giscus[bot]]

latest/how_to/track_objects/

A set of easy-to-use utilities that will come in handy in any computer vision project.

https://supervision.roboflow.com/latest/how_to/track_objects/

[kvnptl]

Hello, can we some other detector instead of yolo?

[onuralpszr]

Hello, can you give us alternative ?

[SkalskiP]

@kvnptl you can connect pretty much any detector you want. We have connectors to all popular frameworks and libraries like Transformers, MMDetection or Detectron2. So any detection model those libraries have you can run with Supervision.

[lange4531]

Hi! Would it work to use the tracking functionality that comes with tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator() but with FastSAM on video data?

[SkalskiP]

Yup. I don't see why not :)

[afifbmazlin]

Can I use this method directly on jetson nano with CSI camera?

[SkalskiP]

We are using supervision on Jetson 100%. Not sure about the CSI camera.

[AlgosDuCouteau]

hi, does supervision support yolov8 version 8.2? or should i stick to yolov8 version 8.1? thank you very much!

[LinasKo]

We aim to support most yolo models coming from ultratlytics. If you can't run Detections.from_ultralytics on model results - let us know!

[AlgosDuCouteau]

thanks, i will give it a shot when have time, thanks again!

[quankun2002]

Hello, How can I track objects and detect if it enter a specified zone and crop it if it does? thank you

[LinasKo]

Hi @quankun2002 👋

Here's a more recent version of the Object counting notebook. It should start you off on object detection and zone counting.
https://github.com/LinasKo/notebooks/blob/fix/modernize-detect-and-count-objects-in-zone/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb

When you have Detections, you can filter them by tracker_id and use the crop_image function to boxes.
Note that detections.xyxy is an array of xyxy values so you'll need to loop over it.

Hope that helps!

[loupspl]

Hi!
If I were to modify this code to pass my own detection results to the tracker, what would be the format of the detection results ?

[LinasKo]

Hi @loupspl 👋

I'm not 100% sure I understood the question, however:
In supervision we provide a universal Detections object and use it as input / output of the many methods we have.

Instead of constructing it manually, I strongly suggest using one of the connectors we have: from_inference, from_ultralytics, from_transfomers cover our most popular frameworks.

Does that answer your question?

[loupspl]

Hi @LinasKo!

Sorry, I wasn't clear enough.

I'm trying to use this project with a custom detector, and its data output apparently do not correspond to any of the expected ouput.

I can chose the output of this detector, with the following items: per frame and per detection: [x1, y1, x2, y2, confidence_score, class_id]

My problem is that it is not formatted, not in the way supervision expects it.

I tried a bit of retroengineering, to build an object usable with the connector from_yolo_v5 but without success.

I also tried bypassing it by building my own detection object to feed the tracker.
From my understanding of looking at the code, it must be an object with xyxy, confidence_score and class_id as attributes, formatted as arrays (n,4), (n), (n) ? Or maybe I should give it a single (n, 6) array ?
Also, am I supposed to give a tracker_id attribute, with an empty (n) array to the detection object, so that the tracker updates it ?

So my question would be: how can I build a detection object similar to what should be given to the tracker and supervision if I'm using a detector not expected from supervision that only gives a list of [x1, y1, x2, y2, confidence_score, class_id] per frame ?
Or what is the connectors that takes something that is the closest to my detection output, so that I can just rebuild an array similar to the expected input of it ?

Thanks in advance if you have the answer to this or for correcting me if I'm missguided!

[LinasKo]

So, the tracker should work on sv.Detections objects and nothing else - that's what you need to create. If a connector we have won't work, then you need to construct it directly. You can use the sv.Detections constructor and refer to the variable definitions for the types the types.

If you have [x1, y1, x2, y2, confidence_score, class_id] then you'll need something similar to this:

• xyxy is np.array([[x1, y1, x2, y2]], dtype=np.float32)
• class_id is np.array([class_id], dtype=int)
• confidence is np.array([confidence_score], dtype=np.float32)

The nested arrays are used when there's more than 1 object detected in the same frame. For example, xyxy might be [[x1, y1, x2, y2], [x1, y1, x2, y2]]. Note you'll then need 2 elements in class_id and confidence too.

When you have the values, call detections = sv.Detections(xyxy=xyxy, class_id=class_id, confidence=confidence).
That should work as an input to the tracker.

[aberg2323]

I found the traceannotator source code which does have a parameter for trace_length but I am unsure on how to actually increase the trace_length in my code

[LinasKo]

Hi @aberg2323,

Let's discuss this in your previous question.
#784 (comment)