Automatic 3D Video Summarization: Key Frame Extraction from Self-Similarity

In this paper we present an automatic key frame selection method to summarise 3D video sequences. Key-frame selection is based on optimisation for the set of frames which give the best representation of the sequence according to a rate-distortion trade-off. Distortion of the summarization from the original sequence is based on measurement of self-similarity using volume histograms. The method evaluates the globally optimal set of key-frames to represent the entire sequence without requiring pre-segmentation of the sequence into shots or temporal correspondence. Results demonstrate that for 3D video sequences of people wearing a variety of clothing the summarization automatically selects a set of key-frames which represent the dynamics. Comparative evaluation of rate-distortion characteristics with previous 3D video summarization demonstrates improved performance.