object-tracking | Multiple Object Tracking System in Keras | Computer Vision library

I am quite intrigued by the idea of a homography and try to get it to work at a minimal example with python and OpenCV. Yet, my tests do not pass and I am not quite sure why. I pass in a set of corresponding points into the findHomography function according to This and then multiply the homography matrix to receive my new point.

so the idea behind it is to find the planar coordinate transformation and then transform the points with

X' = H@X

where X' are the new coordinates and X are the coordinates in the new coordinate frame.

Here is some minimal code example:

Following is a code I found here for real time object tracking:

I'm trying to follow documentation from below GCP link to prepare my video training data. In the doc, it says that if you want to use GCP to label videos, you can use UNASSIGNED feature. I have my videos uploaded to a bucket. I have a traffic_video_labels.csv with below rows:

I have tried to run this code but get an Attribute Error. Any help would be greatly appreciated.

I am following this tutorial to implement object tracking for my project - https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/

Method is to find centroids of detected objects in the initial frame, and then calculate the shortest distance to the other centroids of detected objects that show up on the next frame. Assumption is that a centroid that is closest would be a same object.

In the tutorial -

I have a Django + Python application. I have a python script that takes requirements as command line options.

The issue for me is that when I try to put in the command line arguments the script fails to execute. When I take out the command line arguments, it runs fine. I need those command line arguments.

I am using node JS with Python Shell to execute the python script when the button is clicked in the django HTML front page.

Here is my code:

I am doing object tacking using backgoundsubtraction and i applied this tutorial https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/ for live stream video its giving output properly. but when i gave sequence of image frames all the ID is being printed on the new image and along with old ID and OLD id is not being removed

the next frame will get the new ID and the previous id will also be printed on it How do i fix this

I need the xy horizontal projection of two objects in a video. I have complete control of the environment so I am putting the camera centred on top. (I am not interested in the video itself, but from the video I also get the measures to calibrate the track to proper units).

I am thinking in using one of the implemented tracking algorithms in opencv. I've been playing around with them running the example codes with sample videos and work more than fine.

My first and very main question is: What is the best marker I can use given that I have the freedom to choose? I was thinking that bright LED's is most likely the easiest. But I am not sure given that this algorithms might work better with complex shapes or objects with more features. Could QR codes work better? The marker would move and tilt that's why I am not certain. The light sounds good and quite robust to tilting.

The second question, which actually is probably interdependent with the first one, is: What is the best tracking algorithm for this scenario? I was reading a bit online. A very nice summary of the tracking methods available is https://www.learnopencv.com/object-tracking-using-opencv-cpp-python/ from where I thought I should use KCF tracker. But, I haven't dig that much into the algorithms and I think I might be using not compatible ideas: like, tracking a color is probably simple in the sense that can be done just by some color filtering and based on the speed use some prediction, etc. But maybe this algorithms that detect many features don't really work well with just a light.

So, again, I guess the question might be reformulated as: what is the best marker/tracking algorithm combination in my particular scenario?

I'm toying around with this tutorial from PyImageSearch on object tracking using openCV. In the template they provide, there is a line that lets you load your own video by adding arguments to argparse.