OpenCV intrusion detection

Question

For a project of mine, I'm required to process images differences with OpenCV. The goal is to detect an intrusion in a zone.

To be a little more clear, here are the inputs and outputs:

Inputs:

• An image of reference
• A second image from approximately the same point of view (can be an error margin)

Outputs:

• Detection of new objects in the scene.

Bonus:

• Recognition of those objects.

For me, the most difficult part of it is to take off small differences (luminosity, camera position margin error, movement of trees...)

I already read a lot about OpenCV image processing (subtraction, erosion, threshold, SIFT, SURF...) and have some good results.

What I would like is a list of steps you think is the best to have a good detection (humans, cars...), and the algorithms to do each step.

Many thanks for your help.

Answer 1

Track-by-Detect, human tracker:

1. You apply the Hog detector to detect humans.
2. You draw a respective rectangle as foreground area on the foreground mask.
3. You pass this mask to "The OpenCV Video Surveillance / Blob Tracker Facility"

You can, now, group the passing humans based on their blob.{x,y} values into public/restricted areas.