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• W2885858323 abstract "Reliable track continuity is an important characteristic of multiple target tracking (MTT) algorithms. In the specific case of tracking multiple ground targets from an aerial platform, challenges arise due to realistic operating environments such as imperfections in the measurement source. Some popular visual detection techniques include Kanade-Lucas-Tomasi (KLT)-based motion detection, difference imaging, and object feature matching. Each of these algorithmic detectors has fundamental limitations in regard to providing consistent measurements. In this paper we present a scalable detection framework that leverages multiple measurement sources. We also present the recursive random sample consensus (R-RANSAC) algorithm in a data fusion architecture that can simultaneously accommodate multiple measurement sources. We demonstrate robust track continuity using post-processed flight data and show real-time computational performance." @default.
• W2885858323 created "2018-08-22" @default.
• W2885858323 creator A5061400658 @default.
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• W2885858323 date "2018-06-01" @default.
• W2885858323 modified "2023-09-27" @default.
• W2885858323 title "Improved Track Continuity in Multi Target Tracking by Fusing Multiple Input Sources" @default.
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• W2885858323 doi "https://doi.org/10.23919/acc.2018.8431792" @default.
• W2885858323 hasPublicationYear "2018" @default.
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