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19 pages, 4894 KiB

Open AccessArticle

Detection and Classification of Human-Carrying Baggage Using DenseNet-161 and Fit One Cycle

by Mohamed K. Ramadan, Aliaa A. A. Youssif and Wessam H. El-Behaidy

Big Data Cogn. Comput. 2022, 6(4), 108; https://doi.org/10.3390/bdcc6040108 - 6 Oct 2022

Cited by 4 | Viewed by 3720

In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentally validated, and the results show that the process is sufficiently precise, faster, and outperforms the existing methods. We achieved an accuracy of between 96% and 98.75% for the binary classification and 96.67% and 98.33% for the multi-class classification. For multi-class classification, the datasets, such as PETA, INRIA, ILIDS, and MSMT17, are re-annotated with one’s direction information about one’s stance to test the suggested approach’s efficacy. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Deep Federated Learning Models)

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28 pages, 2270 KiB

Open AccessReview

Perimeter Intrusion Detection by Video Surveillance: A Survey

by Devashish Lohani, Carlos Crispim-Junior, Quentin Barthélemy, Sarah Bertrand, Lionel Robinault and Laure Tougne Rodet

Sensors 2022, 22(9), 3601; https://doi.org/10.3390/s22093601 - 9 May 2022

Cited by 16 | Viewed by 9118

Abstract

In recent times, we have seen a massive rise in vision-based applications, such as video anomaly detection, motion detection, object tracking, people counting, etc. Most of these tasks are well defined, with a clear idea of the goal, along with proper datasets and evaluation procedures. However, perimeter intrusion detection (PID), which is one of the major tasks in visual surveillance, still needs to be formally defined. A perimeter intrusion detection system (PIDS) aims to detect the presence of an unauthorized object in a protected outdoor site during a certain time. Existing works vaguely define a PIDS, and this has a direct impact on the evaluation of methods. In this paper, we mathematically define it. We review the existing methods, datasets and evaluation protocols based on this definition. Furthermore, we provide a suitable evaluation protocol for real-life application. Finally, we evaluate the existing systems on available datasets using different evaluation schemes and metrics. Full article

(This article belongs to the Special Issue Unusual Behavior Detection Based on Machine Learning)

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10 pages, 1039 KiB

Open AccessArticle

Heterologous Expression of Ilicicolin H Biosynthetic Gene Cluster and Production of a New Potent Antifungal Reagent, Ilicicolin J

by Xiaojing Lin, Siwen Yuan, Senhua Chen, Bin Chen, Hui Xu, Lan Liu, Huixian Li and Zhizeng Gao

Molecules 2019, 24(12), 2267; https://doi.org/10.3390/molecules24122267 - 18 Jun 2019

Cited by 24 | Viewed by 5103

Abstract

Ilicicolin H is a broad-spectrum antifungal agent targeting mitochondrial cytochrome bc1 reductase. Unfortunately, ilicicolin H shows reduced activities in vivo. Here, we report our effort on the identification of ilicicolin H biosynthetic gene cluster (BGC) by genomic sequencing a producing strain, Neonectria sp. DH2, and its heterologous production in Aspergillus nidulans. In addition, a shunt product with similar antifungal activities, ilicicolin J, was uncovered. This effort would provide a base for future combinatorial biosynthesis of ilicicolin H analogues. Bioinformatics analysis suggests that the backbone of ilicicolin H is assembled by a polyketide-nonribosomal peptide synthethase (IliA), and then offloaded with a tetramic acid moiety. Similar to tenellin biosynthesis, the tetramic acid is then converted to pyridone by a putative P450, IliC. The decalin portion is most possibly constructed by a S-adenosyl-l-methionine (SAM)-dependent Diels-Alderase (IliD). Full article

(This article belongs to the Section Natural Products Chemistry)

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