  We propose a method which can detect events in videos by modeling the change
in appearance of the event participants over time. This method makes it
possible to detect events which are characterized not by motion, but by the
changing state of the people or objects involved. This is accomplished by using
object detectors as output models for the states of a hidden Markov model
(HMM). The method allows an HMM to model the sequence of poses of the event
participants over time, and is effective for poses of humans and inanimate
objects. The ability to use existing object-detection methods as part of an
event model makes it possible to leverage ongoing work in the object-detection
community. A novel training method uses an EM loop to simultaneously learn the
temporal structure and object models automatically, without the need to specify
either the individual poses to be modeled or the frames in which they occur.
The E-step estimates the latent assignment of video frames to HMM states, while
the M-step estimates both the HMM transition probabilities and state output
models, including the object detectors, which are trained on the weighted
subset of frames assigned to their state. A new dataset was gathered because
little work has been done on events characterized by changing object pose, and
suitable datasets are not available. Our method produced results superior to
that of comparison systems on this dataset.
