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M. Hörhan, H. Eidenberger:
"New Content-Based Features for the Distinction of Violent Videos and Martial Arts";
in: "Proceedings of the International Conference on Acoustics, Speech, and Signal Processing", issued by: IEEE; IEEE Press, Piscataway, 2013.

Real violence is unwanted content in video portals as it is
forensically relevant in video surveillance systems. Naturally,
both domains have to deal with mass data which makes the
detection of violence by hand an impossible task. We introduce
one component of a system for automated violence detection
from video content: the differentiation of real violence
and martial arts videos. In particular, we introduce two new
feature transformations for jitter detection and local interest
point detection with Gestalt laws. Descriptions are classified
in a two-step machine learning process. The experimental
results are highly encouraging: the novel features perform
exceptionally well and the classification process practically
acceptable recall and precision.

Real violence is unwanted content in video portals as it is
forensically relevant in video surveillance systems. Naturally,
both domains have to deal with mass data which makes the
detection of violence by hand an impossible task. We introduce
one component of a system for automated violence detection
from video content: the differentiation of real violence
and martial arts videos. In particular, we introduce two new
feature transformations for jitter detection and local interest
point detection with Gestalt laws. Descriptions are classified
in a two-step machine learning process. The experimental
results are highly encouraging: the novel features perform
exceptionally well and the classification process practically
acceptable recall and precision.

Violence detection, content-based video analysis, local interest point detection, jitter detection, SVM classification