GENERATING COHERENT NATURAL LANGUAGE ANNOTATIONS FOR VIDEO STREAMS

This contribution addresses generation of natural language annotations for human actions, behaviour and their interactions with other objects observed in video streams. The work starts with implementation of conventional image processing techniques to extract high level features for individual frames. Natural language description of the frame contents is produced based on high level features. Although feature extraction processes are erroneous at various levels, we explore approaches to put them together to produce a coherent description. For extending the approach to description of video streams, units of features are introduced to present coherent, smooth and well phrased descriptions by incorporating spatial and temporal information. Evaluation is made by calculating ROUGE scores between human annotated and machine generated descriptions.