CODEWITHSUNDEEP
pythonopencvcomputer-vision
FranckDi
FranckDi

Reputation: 51

What is the best way to track an object in a video?

I'm trying to learn computer vision and more specifically open-cv in python.

I want to make a program that would track my barbell in a video and show me its path. (I know apps like this exists but I want to make it myself). I tried using the Canny edge detection and the HoughCircles functions but I seem to get everything but a good result.

I have been using this code to find the edges of my image:

gray = cv.cvtColor(src=img, code=cv.COLOR_BGR2GRAY)
blur = cv.blur(gray, (2,2))
canny = cv.Canny(blur, 60, 60)

And then this code to find the circle:

circles = cv.HoughCircles(canny, cv.HOUGH_GRADIENT, dp=2, minDist=1000, circles=None,maxRadius=50)

This is the result: Result

left = original image with detected circle // right = canny image

Is this the right way to go or should I use another method?

Upvotes: 5

Views: 3395

Answers (3)

akms
akms

Reputation: 51

What's your problem exactly? Why do you track the barbell? Do you need semantic segmentation or normal detection? These are important questions. Canny is a very basic approach It' needs a very stable background to use it. That's why there is deep learning to handle that kind of problem If we need to talk about deep learning you can use MaskRCNN, yolvoV4, etc. there are many available solutions out there.

Upvotes: 0

Twenkid
Twenkid

Reputation: 955

If you tweak the parameters of HoughCircles it may recognize the barbell [EDIT: but with more preprocessing, gamma correction, blurring etc., so better not], however OpenCV has many algorithms for such object tracking - only a region from the image has to be specified first (if that's OK).

In your case the object is always visible and is not changing much, so I guess many of the available algorithms would work fine.

OpenCV has a built-in function for selection:

initBB = cv2.selectROI("Frame", frame, fromCenter=False, showCrosshair=True)

See this tutorial for tracking: https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/

The summary from the author suggestion is:

CSRT Tracker: Discriminative Correlation Filter (with Channel and Spatial Reliability). Tends to be more accurate than KCF but slightly slower. (minimum OpenCV 3.4.2)

Use CSRT when you need higher object tracking accuracy and can tolerate slower FPS throughput

I guess accuracy is what you want, if it is for offline usage.

Can you share a sample video?

Upvotes: 1

Sivaram Rasathurai
Sivaram Rasathurai

Reputation: 6333

Train the YOLO model for the barbell to detect barbel object is better than anything you tried with OpenCV. You need at least 500 images. Those images can be found on the internet easily. This tutorial is kick start tutorial on YOLO. Let's give a try.

Upvotes: 2

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