An Online Support Vector Machine for Abnormal Events Detection
Manuel Davy, Frederic Desobry, Arthur Gretton and Christian Doncarli
The ability to detect online abnormal events in signals is essential in many realworld
Signal Processing applications. Previous algorithms require an explicit signal
statistical model, and interpret abnormal events as statistical model abrupt changes.
Corresponding implementation relies on maximum likelihood or on Bayes estimation
theory with generally excellent performance. However, there are numerous cases
where a robust and tractable model cannot be obtained, and model-free approaches
need to be considered. In this paper, we investigate a machine learning, descriptorbased
approach that does not require an explicit descriptors statistical model, based
on Support Vector novelty detection. A sequential optimization algorithm is introduced.
Theoretical considerations as well as simulations on real signals demonstrate
its practical efficiency.