Real-time On-line Learning of Transformed Hidden Markov Models from Video
The transformed hidden Markov model is a temporal model that captures three typical causes of variability in video scene/object class, appearance variability within the class, and image motion. In our previous work, we showed that an exact EM algorithm can jointly learn the appearances of multiple objects and/or poses of an object, and track the objects or camera motion in video, starting simply from random initialization. As such, this model can serve as a basis for both video clustering and object tracking applications. However, the original algorithm requires a significant amount of computation that renders it impractical for video clustering and its off-line nature makes it unsuitable for real-time tracking applications. In this paper, we propose a new, significantly faster, on-line learning algorithm that enables real-time clustering and tracking. We demonstrate that the algorithm can extract objects using the constraints on their motion and also perform tracking while the appearance models are learned. We also demonstrate the clustering results on an example of typical unrestricted personal media the vacation video.