yolov4-deepsort VS treecounter-ML

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.

You can take a look at an example implementation of the code here. I was wondering how I can extract the feature maps from a particular layer using the dnn module. I thought of using the net.forward() to do it, but I am not sure if this is the correct way, and more importantly, I do not want to run two forward passes on the same image(I do not think it will be an elegant implementation).

Overlapping Bounding Boxes: As you can see from the image, I have quite a lot of overlapping bounding boxes around each detected object. I did some reading online, and found that I needed to use Non Maxima Suppression to filter the detections. I did that, and adjusted the NMS_THRESH parameter(The current results are at confidence threshold of 95% and NMS threshold of 100%). I am perplexed by this, and what I would like to have is some sort of a filter, where say if I already have a box at (x, y, w, h), then there should be like a barrier around the surrounding n pixels, where even if a box is detected, it will not be placed. If you want to take a look at my code, you can find the repository here and the particular file here.

I am not being able to work the Sort tracker into my current application code. It would be great if any of you could help me out here. The program crashes when I use the tracker.update() method.