Publications
Extracting meaningful information from video sequences for intelligent searches
Video and image data are knowledge-rich sources of information, but their utility for current and future systems is limited without autonomous methods for understanding and characterizing their content. Semantic-based video understanding may benefit systems dedicated to the detection of insiders, alarm patterns, unauthorized activities in material monitoring applications, etc. A direct benefit of this technology is not only intelligent alarm analysis, but the ability to browse and perform query-based searches for useful and interesting information after video data has been acquired and stored. These searches can provide a tremendous benefit for use in intelligence agency, government, military, and DOE site investigations. This report provides an initial investigation into the algorithms and methods needed to characterize and understand video content. Such algorithms include background modeling, detecting dynamic image regions, grouping dynamic pixels into coherent objects, and robust tracking strategies. With solid approaches for addressing these problems, analysis can be performed seeking to recognize distinctive objects and their motions leading to semantic-based video searches.