Abstract:
Assistive software such as screen readers are unable to describe images or videos for visually
impaired people. Although recent research have found ways to automatically describe images,
automatically describing the content of a video is still an ongoing issue. Visually impaired people
find it difficult to understand video content without an indication of sound. The current solution
of video description is only provided through digital television for selected programs and movies.
Since descriptions are manually added extra cost, time and effort is needed. As an initiative to
describe video content for visually impaired people, the solution act as video player which
automatically understand the ongoing human action on screen, associate textual descriptions and
narrate it to the blind user.
The human actions in the video should be recognized in real time, hence fast, reliable feature
extraction and classification method must be adopted. A feature set is extracted for each frame and
is obtained from the projection histograms of the foreground mask. The projection histograms
contains the number of moving pixels for each row and column of the frame. These values provide
sufficient information to identify the instant position of a person. Support Vector Machine is used
to classify extracted features of each frame. The final classification is given by analyzing frame
wise classifications in segments. The classified actions will be converted from text to speech.
