This article introduces the Differential Entropy-based Compactness Index (DECI), a new metric for synthetically describing the spatial distribution of point clouds. DECI is founded on the differential entropy (DE) of point clouds, and if they depict a moving object distribution, the index enables real-time monitoring. Historical data analysis allows for the study of DECI trends and average values in defined intervals. Multiple practical applications are suggested, including risk assessment, congestion measurement, traffic control (including autonomous systems), infrastructure planning, crowd density, and health analysis. DECI’s real-time and historical insights are valuable for decision-making and system optimization and hold potential as a feature in machine learning applications.
DECI: A Differential Entropy-Based Compactness Index for Point Clouds Analysis: Method and Potential Applications
Barberi, Emmanuele
;Cucinotta, Filippo;Sfravara, Felice
2023-01-01
Abstract
This article introduces the Differential Entropy-based Compactness Index (DECI), a new metric for synthetically describing the spatial distribution of point clouds. DECI is founded on the differential entropy (DE) of point clouds, and if they depict a moving object distribution, the index enables real-time monitoring. Historical data analysis allows for the study of DECI trends and average values in defined intervals. Multiple practical applications are suggested, including risk assessment, congestion measurement, traffic control (including autonomous systems), infrastructure planning, crowd density, and health analysis. DECI’s real-time and historical insights are valuable for decision-making and system optimization and hold potential as a feature in machine learning applications.Pubblicazioni consigliate
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