multi-object-tracker VS yolov4-deepsort

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.

I have a video stream of a hiking trail and am trying to count the number of hikers per day. The camera never moves. At night you can also see headlamps :). Direction not as important as just a count of bodies passing through the image in a given time period. https://drive.google.com/file/d/1BwLzyHhrLafyAj5kYn-AfDVBtBBVQCsO/view. I've tried https://github.com/theAIGuysCode/yolov4-deepsort on the stream but it isn't picking up the people, maybe they're too low res for TF Object Detection?

I am trying to track objects in a sequence of images in order to count them. I was looking around for robust trackers since in my case, the camera moves with respect to the object. I found the DeepSORT tracker online and it seems like the solution to my problem. However, I am not sure of how I could train it for my own custom classes. I am currently looking at this repository and it seems to almost do the things I want, except for the counting part. Can anyone explain to me how I can train the DeepSORT tracker for my own classes? I am already training a YOLOv4 model on these custom classes. As a result, I have collected a labelled dataset for the training and validation purposes, and if I have to use images for the training.

I have tried to implement the YOLOv4 + DeepSort tracking from the AI Guys code presented here, just to get an idea of how to go about this, but from what I understand, I will need to train the DeepSORT tracker for detecting my own classes. I do not know how to do that.

You can try a detection and tracking paradigm for videos - it would be a lot more accurate than detection only without the need re-detect every frame which spends a lot of compute. E.g.: https://github.com/theAIGuysCode/yolov4-deepsort

And totally unecessary!! Next day, I discovered a GitHub project that simply used conda, as below.