multiple object detection and tracking

Question

I am new to image processing and I'm working on a project multiple object detection(any object in frame) and tracking. I read few of the detection algorithms and thought of implemnting Gaussian mixture model for detection and tracking using kalman filter.

1) How good is GMM compared to other background subtraction methods?

2) Since GMM uses k-means to separate the foreground from the background. I have been trying to find out the optimal number of K gaussian distributions? Any method/algorithm to find K?

3) GMM detects only moving objects. How can I detect static object?

I want to implement this project using Java in ImageJ.

Any suggestions? Plz help!!

Answer 1

I am not expert in Java but I can tell you what to do: The first mog used to perform background subtraction is in 1999. Although there are many algorithms that tried to optimize the mog but two basic problems still exist: 1- the sensitivity to changes in intensity value " light variations" is very high, 2- it is slow in adaptation which makes a trade off between detecting slow moving objects and avoiding ghosts ( holes result from moving objects) If you just started working on such problem I recommend use: vibe or any of it is extensions: vibe+, vibe++, lobster, subsense. These methods do not fit any model based on prob, this is why they are efficient.

For static objects : this is completely different problem. You can use proposals generation algorithm (edge boxes) to suggest proposals. These proposals can be classified by machine learning. You need to know that these problems still under research and there is no optimal solution