ADAPTIVE AND INTELLIGENT SYSTEMS: Models of Photometric, Geometric and Dynamic Characteristics of Video Imagery for Segmentation, Classification and Synthesis, Including Layers

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Intellectual Merit:The objective of this research is to develop stochastic dynamical models of video imagery for the purpose of synthesis and classification of spatio-temporal events in video.The approach is based on exploiting tools from dynamical systems theory, statistical signal processing, differential geometry and functional analysis in order to infer the spatio-temporal statistics of a video segment and learn (identify) a dynamical model that represents its "signature". This allows generating novel portions of a video segment, or recognizing it in previously unseen video. The investigators will develop identification algorithms for such models, segmentation schemes to partition their spatio-temporal domain into statistically coherent regions, and endow them with a metric structure to enable classification and recognition.Broader Impacts:The models developed will allow the generation of synthetic portions of video, and the manipulation of their spatio-temporal statistics, which is relevant for compression and transmission, and post-production editing and development of interactive games. Furthermore, these models support classification tasks, including detection of events of interest in video and segmentation into spatio-temporal segments. This is important for video-based recognition in security, surveillance, video coding, and environmental monitoring (remote detection of fire, smoke, steam). A particular class of spatio-temporal processes studied includes human motion. The investigators will develop analytical and computational tools to enable the detection and recognition of individuals and their gait from video data. Training students in such a diverse set of analytical and computational tools is a challenge, but one that must be tackled in a modern engineering academic environment.