What’s going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes

We present two novel methods to automatically learn
spatio-temporal dependencies of moving agents in complex
dynamic scenes. They allow to discover temporal rules,
such as the right of way between different lanes or typical
traffic light sequences. To extract them, sequences of
activities need to be learned. While the first method extracts
rules based on a learned topic model, the second
model called DDP-HMM jointly learns co-occurring activities
and their time dependencies. To this end we employ Dependent
Dirichlet Processes to learn an arbitrary number
of infinite Hidden Markov Models. In contrast to previous
work, we build on state-of-the-art topic models that allow
to automatically infer all parameters such as the optimal
number of HMMs necessary to explain the rules governing
a scene. The models are trained offline by Gibbs Sampling
using unlabeled training data.