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This paper presents the status of a R&D project targeting the development of computer-vision tools to assist humans in generating and rendering video description for people with vision loss. Three principal issues are discussed: (1) production practices, (2) needs of people with vision loss, and (3) current system design, core technologies and implementation. The paper provides the main conclusions of consultations with producers of video description regarding their practices and with end-users regarding their needs, as well as an analysis of described productions that lead to propose a video description typology. The current status of a prototype software is also presented (audio-vision manager) that uses many computer-vision technologies (shot transition detection, key-frame identification, key-face recognition, key-text spotting, visual motion, gait/gesture characterization, keyplace identification, key-object spotting and image categorization) to automatically extract visual content, associate textual descriptions and add them to the audio track with a synthetic voice. A proof of concept is also briefly described for a first adaptive video description player which allows end users to select various levels of video description.

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We describe a new method to find and cluster recurrent keyplaces in a movie. It consists of an unsupervised classification of shots that are taking place in the same physical location (key-place). Our approach is based on finding links between key-frames belonging to a same keyplace. We use a probabilistic latent space model over the possible match points between the image sets. This allows extracting significant groups of local descriptor matches that may represent characteristic elements of a key-place. A preliminary test on a full-length movie gives a recognition rate of 78.0% on the key-places clustering.