Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Improve detection box filtering strategies #30

Closed
mhejrati opened this issue Apr 9, 2020 · 4 comments · May be fixed by galliot-us/smart-social-distancing#11
Closed

Improve detection box filtering strategies #30

mhejrati opened this issue Apr 9, 2020 · 4 comments · May be fixed by galliot-us/smart-social-distancing#11
Assignees
Labels
enhancement New feature or request good first issue Good for newcomers RFC Request for comments social distancing

Comments

@mhejrati
Copy link
Contributor

mhejrati commented Apr 9, 2020

We can improve detections using:

  • Scene priors
  • Background/Foreground subtraction and modeling

@alpha-carinae29 & @mrn-mln I suggest you take a look at this.

@mhejrati mhejrati created this issue from a note in Smart Social Distancing (Backlog) Apr 9, 2020
@mhejrati mhejrati added enhancement New feature or request good first issue Good for newcomers RFC Request for comments social distancing labels Apr 9, 2020
@alpha-carinae29
Copy link
Contributor

@mhejrati please move this issue to fine-tuning and calibration milestone

@alpha-carinae29
Copy link
Contributor

I worked on this issue in last days.
I have implemented a background subtraction with cv.createBackgroundSubtractorMOG2 module.
here is a result on Oxford Town Center:
photo_2020-05-17_02-07-53
photo_2020-05-17_02-07-57
Now I am trying to filter object detection's output based on this information. To doing this I will filter boxes that the percentage of background pixels more than a threshold.

@alpha-carinae29
Copy link
Contributor

Also I am implementing a prior distribution of bounding boxes for each pixel in the image. (only mean and variance not the whole distribution.)
To this end I computed the average and variance of width and height of bounding boxes for the centroids of the boxes recurrently with Welford algorithm. After the training Phase, for each detected bounding box we compare its width and height with the prior mean and variance and if we detect an outlier we eliminate the bounding box.

@alpha-carinae29
Copy link
Contributor

@mhejrati please mark it as "in progress".

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
enhancement New feature or request good first issue Good for newcomers RFC Request for comments social distancing
Projects
No open projects
Development

Successfully merging a pull request may close this issue.

2 participants