The ups and down of graphs!!!!

After having some issues I managed to get my graphs for the different settings for the HoG detector and this is what they look like.

This is the number of correct detections for a few HoG settings

This is the number of offsets from the Ground truth for a few HoG Settings

 

 

 

This is only based upon a few frame so that it did not take forever to complete. The graphs as well only contain a select few HoG settings, otherwise it would be to busy and un readable.

I am currently working on trying to try different values between bigger numbers. This will allow me to try more combinations without trying all the combinations between.

Performance issues and graphs

Well things progressing if not a little slower than expected. Currently working on making some graphs and doing a mean shift for the HoG(Histogram of orientated gradients).

I have been creating some files to generate statistics for the different scenarios. The xml files generated have the frame number and HoG and moving average for that frame. This is so I have a file to go through to generate a graph from or use in the future for any data analysis. I also have a python file to generate the ground truth for images. This is done by popping up images and then the user enters the number of people in the frame and it creates an xml entry in the file given. At the moment it keeps the key codes in so they need to be changed to be the actual number of people in the frame.

With the xml statistics files that I have generated for variations on the same scene I am creating a graph against the ground truth to see which one performs better. I started doing this in PyGal but am now looking at MatPlotLib to see if any better. The graph in PyGal is a little claustrophobic see below for current graph from PyGal against graph from MatPlotLib.

PyGal graph

MatPlotLib graph

Looking at the graphs the MatPlotLib graph looks a little neater so will decide to go with that one. Also more graphs to follow.

The moving average of the HoG detector is good when the HoG detector is more stable with a high frame sample set. This is because the average does not deviate too much but when it has a large error it stays incorrect a lot longer. With the 2 frame sample set it adjusts more rapidly.

The next task to look at doing is trying variations of the HoG detector values on a set of images to determine the best settings to use. With the best HoG detector values set I can then re run the graph generators and see what the improvement is. Then I will implement a mean shift on the HoG detector to track people through the scene. This will give me some visuals on the movement of people with in the scene.

If I manage to get that far before my next meeting I will look at tweaking the HoG detector to get a closer box around the people detected to increase the accuracy of the mean shift.