multi-object-tracker VS OpenCV

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.

cd C:\sources git clone https://github.com/opencv/opencv.git // create the opencv directory git clone https://github.com/opencv/opencv_contrib.git // create the opencv_contrib directory

git clone https://github.com/opencv/opencv.git cd opencv git checkout 4.x

To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but contextualise them with human-like reasoning. Start building, and you’ll shape how tomorrow’s machines perceive reality.

Ideal For: Computer vision, NLP, deep learning, and machine learning

Note that the 'Jump Flood Algorithm' is O(N log N) where N is the number of pixels. There is a better O(N) algorithm which can be parallelized over the number of rows/columns of an image:

https://news.ycombinator.com/item?id=36809404

Unfortunately, it requires random access writes (compute shaders) if you want to run it on the GPU. But if CPU is fine, here are a few implementations:

JavaScript: https://parmanoir.com/distance/

C: https://github.com/983/df

C++: https://github.com/opencv/opencv/blob/4.x/modules/imgproc/sr...

Python: https://github.com/pymatting/pymatting/blob/afd2dec073cb08b8...

OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision.

This library is used for image and video processing, offering functions for tasks like object detection, filtering, and transformations in computer vision.

Download the Haar cascade XML files for face and smile detection: https://github.com/opencv/opencv/tree/master/data/haarcascades