Inquiry on Replicating Experiments in Enhanced Tracking and ReID through Segmentation about yolo_tracking HOT 5 CLOSED

Segmentation model vs. object detection model on MOT metrics

You will need to make custom modifications. Pass both bboxes and masks to the tracker and mask the crops generated with bboxes using the masks:

Segmentation model vs. object detection model on MOT metrics

Segmentation models generate both segmentations and bboxes. Only pass the bboxes 😄

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could you briefly explain the specific implementation of "Masked detection crops vs. regular detection crops for ReID feature extraction,"

Everything you need to know regarding this is in this branch:

how it differs in code and approach from the "Segmentation model vs. object detection model on MOT metrics"?

Segmentation models also generate bboxes. These bboxes can be passed instead of the object detection bboxes.

from yolo_tracking.

Got it! So the "Segmentation model vs. object detection model on MOT metrics" experiment refers to model performance between yolov5m and yolov5m-seg(use box output). And the "Masked detection crops vs. regular detection crops for ReID feature extraction" experiment refers to performance comparison between yolov5m-seg and yolov5m-seg with masked refined features, which is related to the code you mentioned.
Thank you for your enlightening response!

from yolo_tracking.

Dear Mike,

I am deeply grateful for your quick and detailed response to my earlier question. It provided valuable insights!

Another question, could you briefly explain the specific implementation of "Masked detection crops vs. regular detection crops for ReID feature extraction," particularly focusing on how it differs in code and approach from the "Segmentation model vs. object detection model on MOT metrics"? I'm also curious about why segmentation enhances tracking in ReID(Masked detection crops vs. regular detection crops for ReID feature extraction) but not as much in the MOT metrics experiment(Segmentation model vs. object detection model on MOT metrics).

Thank you once again for your time and expertise!

from yolo_tracking.

Exactly. Good luck with your experiments 😄

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