  Surveillance system (SS) development requires hi-tech support to prevail over
the shortcomings related to the massive quantity of visual information from
SSs. Anything but reduced human monitoring became impossible by means of its
physical and economic implications, and an advance towards an automated
surveillance becomes the only way out. When it comes to a computer vision
system, automatic video event comprehension is a challenging task due to motion
clutter, event understanding under complex scenes, multilevel semantic event
inference, contextualization of events and views obtained from multiple
cameras, unevenness of motion scales, shape changes, occlusions and object
interactions among lots of other impairments. In recent years, state-of-the-art
models for video event classification and recognition include modeling events
to discern context, detecting incidents with only one camera, low-level feature
extraction and description, high-level semantic event classification, and
recognition. Even so, it is still very burdensome to recuperate or label a
specific video part relying solely on its content. Principal component analysis
(PCA) has been widely known and used, but when combined with other techniques
such as the expectation-maximization (EM) algorithm its computation becomes
more efficient. This chapter introduces advances associated with the concept of
Probabilistic PCA (PPCA) analysis of video event and it also aims at looking
closely to ways and metrics to evaluate these less intensive EM implementations
of PCA and KPCA.
