Kalman away

Well took long enough but I have a Kalman filter working. Thanks to help and code from my dissertation supervisor Hannah Dee. I have managed to tweak her code that uses Kalman2d which can be found here to fit in with my scenario. Example of what it is currently doing can be seen below.

Here shows the Kalman filter in action(the green line) however at this point it jumps between the two people in the box currently

I have added some basic checking for the HoG(Histogram of orientated gradients) and Kalman filter so that if a HoG is not detected the bounding box is drawn in the last place it was located. If it then re appears the Kalman filter then is updated with the new location. This is done by searching with a buffer with in the range of last HoG detection, this is assuming the person has not travelled far and there is no one else nearer.

The issues I am having with two people coming near to each other that the tracker swaps over is not an issue. The reason being as if two people come close to each other and one gets the tracker from the other then the other one will get their tracker. One way I can mitigate this is to start the search in the direction the person was travelling so that way the person is more likely to get their tracker back.

I am currently only tracking one person but now I have a goal I feel I can really push this through. My goal is to build a person object that will keep track of locations to help mitigate occlusions and count them. This will be using HoG detector as the initialiser, mean shift, Kalman filter and cam shift to help determine location helping keep a more accurate track of people. Background subtraction will be used as evidence to show that something is moving here chances are someone or something is here.

I am hoping to be able to determine in scene exits by determining if a number of person detectors go dead at the same location. If they are predicted to go off scene then that is a normal exit but can monitor exits to focus detectors more.

Any way this will be my last blog post for a while as I am off to sunny Lloret de Mar for my first vacation abroad with dance sport. Should be a good time sportsvest who are organising it have made it look like it is going to be an epic time. So speak in a week and a bit.

Lloret de Mar

Kalman filter pain

So over the past weeks I have been having some issues trying to get the Kalman filter to work to predict location of the people in the scene. This is going to be used to determine if they are in scene but occluded so still need to count them.

Several problems I was having involved everywhere I looked the tutorials told me to use something which wasn't in the OpenCV version I had or wasn't recognised. As such I was upgraded OpenCV to 2.4.11 rather than my current
version, 2.4.10.

Upgrading OpenCV was more of an issue than expected and had a few issues with the building. My first issue was I ran out of space on my laptop.

Turns out issue I was having was with using cv2 not cv which I just thought the way the examples were named. So after importing cv2.cv as cv I managed to get the Kalman filter working. Now I can start using it to determine where people are heading. First I need to get it so it stays on one person as HoG(Histogram of orientated gradients) sometimes picks a different person to find first.

 As well as trying to get the Kalman filter working I also did some tests to determine best moving average background subtraction to use. As well as this I calculated the time for completion on a set of 795 images, my PETs 2009 s2l1 data set. Results can be seen in the graphs below.

Compared moving average bg subtraction with photo shopped image using technique from here

I am still working on trying to get a outline of a person for my ROI(Region of interest) rather than a tight box to reduce the errors that are thrown. I am currently very close to getting this working but needs a few more tweaks and should be good.

A bit of house cleaning was in order as I had a few Python files in my directory that needed removing. It still needs a bit of a clean but it is much easier to navigate now.


 When everything is implemented properly then I can start work on different combinations of detection and see which combinations are the most accurate and the fastest. I can try this on other data sets as well to see how well they preform under different circumstances.

With the features implemented I can also catch up on my tests for them. As well as cleaning up my code with more comments and better layout.

Hopefully the next lot of stuff to do after Kalman filter will be a little less frustrating.