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Optical low-based
representation for video
action detection
Samet Akpınar; Ferda Nur Alpaslan Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
In this study, a new model to the problem of video action recognition has been proposed. The model
is based on temporal video representation for automatic annotation of videos. Additionally, the generic
model inspires other video information retrieval processes including temporal video segmentation.
Video action recognition is a field of multimedia research enabling us to recognize the actions from a
number of observations, where representation of temporal information becomes important. Visual, au-
dio, and textual features are important sources for representation. Although textual and audio features
provide high-level semantics, retrieval performance using these features highly depends on the availab-
ility and richness of the resources. Visual features such as edges, corners, and interest points are used for
forming a more complicated feature, namely, optical flow. For developing methods to cope with video
action recognition, we need temporally represented video information. For this reason, we propose a
new temporal segment representation to formalize the video scenes as temporal information. The repres-
entation is fundamentally based on the optical flow vectors calculated for the frequently selected frames
of the video scene. Weighted frame velocity concept is put forward for a whole video scene together with
the set of optical flow vectors. The combined representation is used in the action-based video segment
classiication. Proposed method is applied to significant datasets and the results are analyzed by com-
paring with the state-of-the-art methods. The basic formalism mentioned in the method is also handled
for video cut detection in the perspective of temporal video segmentation.
Video action recognition
Content-based video information retrieval
Optical low
Temporal video segment representation
Weighted frame velocity
Cut detection
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