Semantic Segmentation Based Crowd Tracking and Anomaly Detection via Neuro-fuzzy Classifier in Smart Surveillance System

Abstract Crowd tracking and analysis of crowd behavior is a challenging research area in computer vision. In today’s crowded environment manual surveillance systems are inefficient, labor-intensive, and unwieldy. Automated video surveillance systems offer promising solutions to these problems and he...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Abdullah, Faisal [verfasserIn]

Jalal, Ahmad

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2022

Schlagwörter:

Attraction force model

Crowd shape deformation

Multilayer neuro-fuzzy classifier

Semantic segmentation

Time-domain descriptors

Tracking and anomaly detection

Anmerkung:

© King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:

Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 48(2022), 2 vom: 25. Aug., Seite 2173-2190

Übergeordnetes Werk:

volume:48 ; year:2022 ; number:2 ; day:25 ; month:08 ; pages:2173-2190

Links:

Volltext

DOI / URN:

10.1007/s13369-022-07092-x

Katalog-ID:

SPR049282018

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