Points, assumptions and more graphs!

Ok so it has been a busy week so here comes a list of what I have been up to with my dissertation. Don't worry there are pictures.

First thing I got working was MoG(Mixture of Gaussian) background subtraction using this page on OpenCV. This is to allow me to detect movement within the image that can help determine the likeliness of a person existing at that point. I have also implemented moving average background subtraction from this helpful site. I will be creating a test to try different values for moving average background subtraction to see which one is the best to use.

I have also implemented Cam shift which is starts off by doing a mean shift but then updates the size of the window and calculates the best rotation of a fitting ellipse to it. Then re applies the mean shift with the new scaled search window and previous window position. This is then repeated until the desired accuracy is met. (Paraphrased from here).


I am dealing with the problem of people being obscured by using a Kalman filter to determine the direction and velocity of people. From this I can determine if they are going to end up off screen or if they are still with in the scene but obscured. The issue may arise if there is an exit on screen that the person can disappear through. This issue can be dealt with by assuming that if the person isn't detected with in a number of frames then they are likely no longer in the scene. I won't be modelling the scene as that is a whole other project.


I have also made some more wonderful graphs to show the number of correct detections that each setting for the HoG detector provides. These can be seen below

Patterns include: Multiples of 4 having 0 detections and multiples of 6 having highest number of detections.

Steady drop of accuracy after scale setting of 1.01

This peaks at 3 but may go back up for higher numbers

My code is getting a little messier than I would like it to be. For this reason I will be going over my code and modularising it and doing some more testing for the new code I have done.


I am working on a points based system that will give points to the more accurate detections that are all consistent. This will use mean shift, cam shift and hog detectors to build up a more accurate person detector. I will create an array of ROIs(regions of interest) for each person depending on the score they get to build up what to look for.


I have encountered some problems along the way. Some of these include contours from background subtraction overlapping with other HoG boundaries meaning false positive when looking for movement in a HoG detection. Another issue is a 'non convex optimisation problem' with trying to search through the most accurate window stride for HoG detector. This is trying to avoid searching through all the possible combinations by going in the direction of the best detector settings in chunks.


An interesting point that was raised in a dissertation meeting was, should you count people that are occluded. If a person was counting number of people in a scene and a person was occluded that person would not be counted. There may be times when you want to know if a person is still in a scene even when obscured. An example would be when a place is limited to a certain number of people but they will be blocked by certain elements. In this case keeping track of number of people is important.

In the case of my dissertation I will be keeping track of all people that are judged to still be with in the scene.