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This research presents a new model-based approach toward the three-dimensional (3-D) tracking and extraction of gait and human motion. We suggest the use of a hierarchical, structural model of the human body that introduces the concept of soft kinematic constraints. These constraints take the form of a priori, stochastic distributions learned from previous configurations of the body exhibited during specific activities; they are used to supplement an existing motion model limited by hard kinematic constraints. We use time-varying parameters of the structural model to measure gait velocity, stance width, stride length, stance times, and other gait variables with multiple degrees of accuracy and robustness. To characterize tracking performance, we also introduce a novel geometric model of expected tracking failures. We demonstrate and quantify the performance of the suggested models using multi-view, video sequences of human movement captured in a complex home environment.

With the advent of advances in Geospatial Information Systems (GIS); there is a need to determine the areas of research and new tools available for GIS systems. GIS consists of the collection, integration, storage, exploitation, and visualization of geographic and contextual data and spatial information. Future GIS needs, techniques, models, and standards should be shared openly among developers for future instantiation of products. The summary of selected areas include (1) support for large-data formats including meta-data transparency, (2) adherence to open standards, (3) generation of extensible architectures, and (4) development of a consistent set of metrics for analysis. The future of GIS products will include non-spatial as well as spatial data which requires information fusion, management functions from machine-processed data to user-defined actionable information, and use-case challenge problems for comparison.