KEYWORDS: Video, Video surveillance, Kinematics, Defense and security, Logic, Detection and tracking algorithms, Surveillance, Video processing, Data modeling, Computer security
The adversary in current threat situations can no longer be identified by what they are, but by what they are doing. This
has lead to a large increase in the use of video surveillance systems for security and defense applications. With the
quantity of video surveillance at the disposal of organizations responsible for protecting military and civilian lives comes
issues regarding the storage and screening the data for events and activities of interest.
Activity recognition from video for such applications seeks to develop automated screening of video based upon the
recognition of activities of interest rather than merely the presence of specific persons or vehicle classes developed for
the Cold War problem of "Find the T72 Tank". This paper explores numerous approaches to activity recognition, all of
which examine heuristic, semantic, and syntactic methods based upon tokens derived from the video.
The proposed architecture discussed herein uses a multi-level approach that divides the problem into three or more tiers
of recognition, each employing different techniques according to their appropriateness to strengths at each tier using
heuristics, syntactic recognition, and HMM's of token strings to form higher level interpretations.
In this paper we address the registration of close range imagery to virtual urban models, using buildings and other fixed objects in a scene. We introduce a novel approach, using radiometric and spatial queries to support the registration of ground level imagery. Image registration involves the comparison of an image’s content to the information contained in a VR model, to identify in the VR model the facades that best resemble the ones contained in the processed imagery. This registration-through-queries approach allows us to use coarse information in the form of imprecisely outlined facades to perform image registration, removing the requirements for time-consuming processes like precise delineation of control point measurement. In the paper we introduce radiometric indexing schemes to support object facade queries, and present experiments to demonstrate the function of these metrics in our image registration framework.
Handling change within integrated geospatial environments is a challenge of dual nature. It comprises automatic change detection, and the fundamental issue of modeling/representing change. In this paper we present a novel approach for automated change detection which allows us to handle change more efficiently than commonly available approaches. More specifically, we focus on the detection of building boundary changes within a spatiotemporal GIS environment. We have developed a novel approach, as an extension of least-squares based matching. Previous spatial states of an object are compared to its current representation in a digital image, and decisions are automatically made as to whether or not change at the outline has occurred. Older object information is used to produce templates for comparison with the representation of the same object in a newer image. Semantic information extracted through an analysis of template edge geometry, and estimates of accuracy are used to enhance our model. This template matching approach allows us to integrate in a single operation object extraction from digital imagery with change detection. By decomposing a complete outline into smaller elements and applying template matching along these locations we are able to detect precisely even small changes in building outlines. In this paper we present an overview of our approach, theoretical models, certain implementation issues like template selection and weight coefficient assignment, and experimental results.
KEYWORDS: 3D image processing, Chemical elements, Image processing, Tomography, Solid state cameras, Visualization, Seaborgium, Photogrammetry, Image analysis, Imaging systems
We present an image analysis technique for examining the fine scale (Kolmogorov scale) variations of the mixing process in a turbulent flow. The objective is to trace the interaction of two flows by identifying their motions in a sequence of 3-D images obtained with a system based on a high-speed solid state camera. After briefly describing the imaging process and the particularities related to the capture of quasi-continuous 3-D image sequences, we focus on theoretical and implementational issues associated with feature tracking in 3-D image sequences. We present the extension of least squares matching from pixels, associated with 2- D images, to voxels, associated with 3-D images. The use of additional constraints of radiometric and/or geometric nature strengthens the matching solution. In addition, the large amount of data associated with 3-D image sequences in general, and the high and multidirectional velocities involved in this application in particular, make the division of an efficient matching strategy quite important.
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