16 May 2011 Target tracking with GIS data using a fusion-based approach
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Abstract
Military forces and law enforcement agencies are facing new challenges for persistent surveillance as the area of interest shifts towards urban environments. Some of the challenges include tracking vehicles and dismounts within complex road networks, traffic patterns and building structures. Under these conditions, conventional video tracking algorithms suffer from target occlusion, lost tracks and stop-and-start. Furthermore, these algorithms typically depend solely on pixel-based features to detect and locate potential targets, which are computationally intensive and time consuming. This research paper investigates the fusion of geographic information into video-based target tracking algorithms for persistent surveillance. A geographic information system (GIS) has the capability to store attributes about a target's surroundings - such as road direction and boundaries, intersections and speed limit - and can be used as a decision-making tool in prediction and analysis. Fusing this prediction capability into conventional video-centric target tracking algorithms provides geographical context to the target feature space improves occlusion of targets and reduces the search area for tracking. The GIS component specifically improves the performance of target tracking by minimizing the search area a target is likely to be located. We present the results from our simulations to demonstrate the feasibility of the proposed technique with video collected from a prototype persistent surveillance system. Our approach maintains compatibility with existing GIS databases and provides an integrated solution for multi-source target tracking algorithms.
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Brian Bradford, Eric M. Dixon, Joshua Sisskind, William D. Reynolds, "Target tracking with GIS data using a fusion-based approach", Proc. SPIE 8053, Geospatial InfoFusion Systems and Solutions for Defense and Security Applications, 80530F (16 May 2011); doi: 10.1117/12.883424; https://doi.org/10.1117/12.883424
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