  Video processing is generally divided into two main categories: processing of
the entire video, which typically yields optimal classification outcomes, and
real-time processing, where the objective is to make a decision as promptly as
possible. The latter is often driven by the need to identify rapidly potential
critical or dangerous situations. These could include machine failure, traffic
accidents, heart problems, or dangerous behavior. Although the models dedicated
to the processing of entire videos are typically well-defined and clearly
presented in the literature, this is not the case for online processing, where
a plethora of hand-devised methods exist. To address this, we present \our{}, a
novel, unified, and theoretically-based adaptation framework for dealing with
the online classification problem for video data. The initial phase of our
study is to establish a robust mathematical foundation for the theory of
classification of sequential data, with the potential to make a decision at an
early stage. This allows us to construct a natural function that encourages the
model to return an outcome much faster. The subsequent phase is to demonstrate
a straightforward and readily implementable method for adapting offline models
to online and recurrent operations. Finally, by comparing the proposed approach
to the non-online state-of-the-art baseline, it is demonstrated that the use of
\our{} encourages the network to make earlier classification decisions without
compromising accuracy.
