Delta-Dual Hierarchical Dirichlet Processes
The 13th International Conference on Computer Vision (2011) is just days away, and my paper promises code.

So, my paper is:

Delta-Dual Hierarchical Dirichlet Processes: A pragmatic abnormal behaviour detector

by me, Tom. S. F. Haines, and Tao. Xiang. The above links through to the pdf, as you would expect, and you can also get the poster from here. The code is available from my Google code project, which is accessible from the menu.

The approach itself is an extremely complex topic model (Non-parametric and Bayesian.) designed with video data in mind. Its stated purpose, which we demonstrate, is to be able to do semi-supervised learning of behaviour that is normal, except that it is happening in an unusual context. An example of this is we might want to differentiate between people crossing the road and people crossing the road whilst traffic continues to drive across the crossing. The results are somewhat weak however, due to the difficulty of finding real world examples of such behaviours with enough examples for training/testing. I am also not convinced that it fully converges, or that taking a single sample from such a complex Gibbs sampler is sensible (Which is all we can do given the run time.). The complexity is its main failing however - there are so many random variables being sampled, often using extremely complex techniques, that no sane person would ever reimplement this algorithm. Given how much work was involved I'm still somewhat surprised I got it implemented and working in the first place!