Motion Analysis From Video Streams Based On Syntactic Attribute Graph Grammar

Video motion analysis has broad application areas, such as visual surveillance, video browsing, content-based video indexing and perceptual interface. Video motion analysis aims to achieve image understanding and scene explanation, and then supervise activities. Video motion analysis is also an active research field in computer vision and artificial intelligence.Given the video motion recognition task, this paper studies many algorithms following the framework of video motion analysis, such as object extracting, object tracking, recognition and motion representation etc. And it proposes a new method based attribute graph grammar.As the workflow of video motion analysis, first, the paper introduces the low-level motion analysis inference, and improves these algorithms by adding some pre-processing modules; then, it clusters trajectories of objects by spectral clustering , and extracts motion features, such as location, velocity, is visible etc. Finally, the video motion analysis algorithm based on attribute graph grammar is proposed.This grammar models the variability of semantic events by a set of meaningful "event components" with the spatio-temporal constrains. The event components are further decomposed into atomic event primitives. With this representation, one observed event can be parsed into an "event parse graph", and all possible variability of one event can be modeled into an "event And-Or graph", in a syntactic way representation.Our experiments show the effectiveness of the proposed video motion analysis algorithms based on attribute graph grammar.