Naslov
Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments

Autori
Cuzzocrea, Alfredo ; Lenac, Kristijan ; Mumolo, Enzo

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12923. Springer, Cham

Urednik/ci
Strauss, Christine ; Kotsis, Gabriele ; Tjoa, A Min ; Khalil, Ismail

Izdavač
Springer

Grad
Cham

Godina
2021

Raspon stranica
348-360

ISSN
0302-9743

Ključne riječi
Moving objects ; Scan-matching algorithms ; Intelligent systems ; Genetic optimization

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA