13 June 2013 KOLAM: a cross-platform architecture for scalable visualization and tracking in wide-area imagery
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Proceedings Volume 8747, Geospatial InfoFusion III; 87470N (2013); doi: 10.1117/12.2018162
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States
Abstract
KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi- scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics) hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper- jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection, trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple automated tracking algorithms are available to assist the analyst and increase human effectiveness.
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Joshua Fraser, Anoop Haridas, Guna Seetharaman, Raghuveer M. Rao, Kannappan Palaniappan, "KOLAM: a cross-platform architecture for scalable visualization and tracking in wide-area imagery", Proc. SPIE 8747, Geospatial InfoFusion III, 87470N (13 June 2013); doi: 10.1117/12.2018162; https://doi.org/10.1117/12.2018162
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KEYWORDS
Optical tracking

Visualization

Automatic tracking

Visual analytics

Detection and tracking algorithms

Image visualization

Human-machine interfaces

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