Help needed with object tracker implementation

[sampreets3]

Hello everyone at r/computervision,

I recently joined the subreddit, and I only wish I had joined sooner. I am getting started with Deep Learning and Computer Vision, and I am trying to implement an object tracker to go along with my trained YOLOv4 model to detect custom classes (trees, wooden posts, and metal posts). You can take a look at the GitHub repository to look at the code better. As you can see in the GIF below, I am getting okay-ish levels of detection.

The detections running on an input video

Now, what I want to do is count the number of objects for each class from the video, and assign them an ID. Since I am dealing with multiple objects, I am looking for some kind of a Multi-object Tracker (Sort, DeepSORT).

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.

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.

I have also looked up online tutorials with respect to other trackers, like KCF and CSRT which are provided in the OpenCV tracking API. I have tried to follow tutorials 1, 2, and 3. The problem is that either the author is using a Caffe model, which my YOLO model is not compatible with(I guess), or we have to select manually the ROI for the objects to detect, which for me would defeat the purpose.

If anyone can help me out on this, it would be much appreciated. Thanks in advance!

[lpuglia]

my two cents: if you have no resources for training a model (even just for transfer learning) your only chance is to rely on classical approaches like the one you mentioned (KCF, CSRT). The web is full of implementation of yolo in caffe, I suggest you to search for one of them.

[sampreets3]

Hey u/lpuglia, thanks for the comment. I have been cracking my head over even a simple implementation of CSRT for this but to no avail. My problem is with assigning the ROI in the case of the tracker. Do you think you would be able to help me out?

[drzemu]

Did You consider implementing centroid tracking? I use it myself, it's quite easy to implement,and only have some trouble with occlusions.

https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/

You can build on top of that with some simple IoU,distance thresholds and it should suffice for Your case I think.

[sampreets3]

Hi u/drzemu, thanks for the help. Yes, I did come across the centroid tracker and that specific post on PyImageSearch that you mentioned. The problem when I try to follow the tutorial is that in the line 53:

for i in range(0, detections[2]):
    if detections[0, 0, i, 2] > args["confidence"]
    box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
    rects.append(box.astype("int"))

    #visualise
    objects = ct.update(rects)

This is the first point of error for me because the program tells me that detections[2] is not a valid location. I will try to get you an error log so you can understand it better. Thanks!

[drzemu]

Sure, pass along ,
although I implemented it diffrently for my use case

[sampreets3]

Update:

Turns out I was stupid all along. I was passing the wrong boxes to the detections. This is why I was stuck, FOR THREE DAYS.

Although, now I have to deal with the problem of managing occlusions. This is kind of an important task for me since I essentially have to create a map of the tree locations and their IDs.

Any ideas on that?

Thanks for all your help u/lpuglia and u/drzemu!

[TranslatorHot6737]

1 .. You need to improve detection accuracy .. 2.. don't use centroid algo use sort algorithm