Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments (CROSBI ID 72775)
Prilog u knjizi | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Cuzzocrea, Alfredo ; Lenac, Kristijan ; Mumolo, Enzo
engleski
Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments
Big moving objects arise as a novel class of big data objects in emerging environments. Here, the main problems are the following: (i) tracking, which represents the baseline operation for a plethora of higher-level functionalities, such as detection, classification, and so forth ; (ii) analysis, which meaningfully marries with big data analytics scenarios. In line with these goals, in this paper we propose a novel family of scan matching algorithms based on registration, which are enhanced by using a genetic pre-alignment phase based on a novel metrics, fist, and, second, performing a finer alignment using a deterministic approach. Our experimental assessment and analysis confirms the benefits deriving from the proposed novel family of such algorithms.
Moving objects ; Scan-matching algorithms ; Intelligent systems ; Genetic optimization
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Podaci o prilogu
348-360.
objavljeno
10.1007/978-3-030-86472-9_32
Podaci o knjizi
Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12923. Springer, Cham
Strauss, Christine ; Kotsis, Gabriele ; Tjoa, A Min ; Khalil, Ismail
Cham: Springer
2021.
0302-9743