Kornia VS multi-object-tracker

👉 https://github.com/kornia/kornia/releases/tag/v0.6.6

Kornia: Differential Computer Vision\ (3 comments)

Kornia - Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.

Between "not being sure yet" about GPU operations in pre-processing and choosing high-end CPUs, I think you are overthinking the wrong alternative. Besides DALI, check whether you are using codecs besides nvidia/torchvision-supported jpeg and png, and if other GPU CV libraries meet your needs: torchvision kornia

Use kornia.augmentation where this problem is solved doing the augmentations in batch outside the dataloader. https://github.com/kornia/kornia

You may find this useful: https://adipandas.github.io/multi-object-tracker/

As you can see, in Frame 43, the tracker assigns the ID 10 to a metal post, but a few frames later when the tracker tracks the same metal post, it provides an ID of 11. This shows that the tracker is assigning new IDs to the same detected object. I cannot seem to find out why this happens, and how to fix it. I am using the motrackers module from this repo. I have not made any changes as such in the mot_yolov3.py file, I have just added the line to print the frame counter.

I have a simple python project which has gone through at least 5 iterations since I began working on it. Please see this link: adipandas/multi-object-tracker.