Digit Recognition using Optical Flow Approach

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. 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, audio 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 availability and richness of the resources. Visual features such as edges, corners, interest points etc. 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 representation 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 classification. Proposed method is applied to significant data sets and the results are analyzed by comparing to the state- of-the art methods.