Hybrid Deep Learning Technique with One Class Svm for Anomaly Detection in Crowded Environment

IF 1.2 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Periodico Di Mineralogia Pub Date : 2022-04-18 DOI:10.37896/pd91.4/91442

N. Priyadharsini, R. Kavitha, A. Kaliappan, D. Chitra

引用次数: 0

Abstract

of pattern matching in developed a hybrid deep learning based on a pre-trained Convolution Neural Network and One-class SVM is trained with spatial features for robust classification of abnormal shapes. the experimental the proposed anomaly detection techniques existing techniques in of within a continuous learning setup. Multi cue learning approach presents rule based event detection and multiple feature tracking.

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基于一类支持向量机的混合深度学习技术在拥挤环境下的异常检测

提出了一种基于预训练卷积神经网络的混合深度学习方法，并结合空间特征训练一类支持向量机，对异常形状进行鲁棒分类。实验中提出的异常检测技术，现有的技术，在一个持续的学习设置。多线索学习方法提出了基于规则的事件检测和多特征跟踪。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Periodico Di Mineralogia 地学-地球化学与地球物理

CiteScore

1.50

自引率

14.30%

发文量

审稿时长

>12 weeks

期刊介绍： Periodico di Mineralogia is an international peer-reviewed Open Access journal publishing Research Articles, Letters and Reviews in Mineralogy, Crystallography, Geochemistry, Ore Deposits, Petrology, Volcanology and applied topics on Environment, Archaeometry and Cultural Heritage. The journal aims at encouraging scientists to publish their experimental and theoretical results in as much detail as possible. Accordingly, there is no restriction on article length. Additional data may be hosted on the web sites as Supplementary Information. The journal does not have article submission and processing charges. Colour is free of charges both on line and printed and no Open Access fees are requested. Short publication time is assured. Periodico di Mineralogia is property of Sapienza Università di Roma and is published, both online and printed, three times a year.