Display device color management and visual surveillance of vehicles
Abstract
Digital imaging has seen an enormous growth in the last decade. Today users have numerous choices in creating, accessing, and viewing digital image/video content. Color management is important to ensure consistent visual experience across imaging systems. This is typically achieved using color profiles. In this thesis we identify the limitations of profile-based color management systems and propose an alternative system based on display device models and look-up tables (LUT). We identify techniques to design LUTs which are optimal in terms of color reproduction accuracy under resource constraints. We show that a LUT-based color management system is more accurate and memory-efficient than a comparable ICC profile-based system. Visual surveillance is often used for security and law enforcement applications. In most cases the video data is either passively recorded for forensic applications or is remotely monitored by human operators. We propose the use of image and video analysis techniques to assist the operators. This would reduce human errors due to fatigue, boredom, and excess information. We describe a video surveillance system to observe vehicular traffic from a standoff range and detect anomalous behavior by analyzing the motion trajectories. We also extract physical information (such as make, tire size, and body type) which can help determine the “normal” behavior. The operator can also use this information to uniquely identify/describe individual vehicles. We describe low complexity techniques to perform the above analyses and show their effectiveness on real traffic videos.
Degree
Ph.D.
Advisors
Delp, Purdue University.
Subject Area
Engineering|Information science|Computer science
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