scholarly journals Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 343
Author(s):  
Roxana-Elena Mihaescu ◽  
Mihai Chindea ◽  
Constantin Paleologu ◽  
Serban Carata ◽  
Marian Ghenescu
Keyword(s):  
Detection System ◽  
General System ◽  
Identification Problem ◽  
General Purpose ◽  
Identification System ◽  
Detection Model ◽  
Simple Detection ◽  
Additional Costs ◽  
General Object ◽  
Disjoint Camera Views

Solving the person re-identification problem involves making associations between the same person’s appearances across disjoint camera views. Further, those associations have to be made on multiple surveillance cameras in order to obtain a more efficient and powerful re-identification system. The re-identification problem becomes particularly challenging in very crowded areas. This mainly happens for two reasons. First, the visibility is reduced and occlusions of people can occur. Further, due to congestion, as the number of possible matches increases, the re-identification is becoming challenging to achieve. Additional challenges consist of variations of lightning, poses, or viewpoints, and the existence of noise and blurring effects. In this paper, we aim to generalize person re-identification by implementing a first attempt of a general system, which is robust in terms of distribution variations. Our method is based on the YOLO (You Only Look Once) model, which represents a general object detection system. The novelty of the proposed re-identification method consists of using a simple detection model, with minimal additional costs, but with results that are comparable with those of the other existing dedicated methods.

scholarly journals Precision inclusive Higgs physics at e+e− colliders with tracking detectors and without calorimetry

2020 ◽  
Vol 2020 (9) ◽  
Author(s):  
Patrick Draper ◽  
Jonathan Kozaczuk ◽  
Scott Thomas
Keyword(s):  
Higgs Boson ◽  
Cross Section ◽  
Tracking System ◽  
Inclusive Cross Section ◽  
Nuclear Interaction ◽  
General Purpose ◽  
Boson Mass ◽  
Practical Reasons ◽  
Identification System ◽  
Tracking Detector

Abstract A primary goal of a future e+e− collider program will be the precision measurement of Higgs boson properties. For practical reasons it is of interest to determine the minimal set of detector specifications required to reach this and other scientific goals. Here we investigate the precision obtainable for the e+e−Zhμ+μ−X inclusive cross section and the Higgs boson mass using the di-muon recoil method, considering a detector that has only an inner tracking system within a solenoidal magnetic field, surrounded by many nuclear interaction lengths of absorbing material, and an outer muon identification system. We find that the sensitivity achievable in these measurements with such a tracking detector is only marginally reduced compared to that expected for a general purpose detector with additional electromagnetic and hadronic calorimeter systems. The difference results mainly from multi-photon backgrounds that are not as easily rejected with tracking detectors. We also comment on the prospects for an analogous measurement of the e+e−→Zh→e+e−X inclusive cross section. Finally, we study searches for light scalars utilizing the di-muon recoil method, estimating the projected reach with a tracking or general purpose detector.

scholarly journals Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5315
Author(s):  
Chia-Pei Tang ◽  
Kai-Hong Chen ◽  
Tu-Liang Lin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Early Recognition ◽  
Detection System ◽  
Colon Polyp ◽  
Colon Polyps ◽  
Polyp Detection ◽  
Detection Model ◽  
Real Time Detection ◽  
Polyp Type

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.

scholarly journals Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

2018 ◽  
Vol 8 (9) ◽  
pp. 1628 ◽  
Author(s):  
Shiyang Zhou ◽  
Shiqian Wu ◽  
Huaiguang Liu ◽  
Yang Lu ◽  
Nianzong Hu
Keyword(s):  
Steel Sheet ◽  
Surface Defect ◽  
Detection System ◽  
Saliency Detection ◽  
Image Understanding ◽  
Low Rank ◽  
Sparse Decomposition ◽  
Detection Model ◽  
Otsu’S Method ◽  
Surface Defect Detection

Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.

scholarly journals Network Anomaly Detection System with Optimized DS Evidence Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Liu ◽  
Xiaofeng Wang ◽  
Kaiyu Liu
Keyword(s):  
Anomaly Detection ◽  
Detection Rate ◽  
Computer Network ◽  
Detection System ◽  
Evidence Theory ◽  
Optimization Methods ◽  
High Detection Rate ◽  
Detection Model ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

scholarly journals Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation

Biosensors ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 343
Author(s):  
Chin-Teng Lin ◽  
Wei-Ling Jiang ◽  
Sheng-Fu Chen ◽  
Kuan-Chih Huang ◽  
Lun-De Liao
Keyword(s):  
Eye Movement ◽  
Experimental Validation ◽  
Hardware Implementation ◽  
Detection System ◽  
Real Life ◽  
People With Disabilities ◽  
Research Area ◽  
General Purpose ◽  
Classification Algorithm ◽  
Human Computer Interface

In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

scholarly journals SISTEM PENERIMA (RECEIVER) AUTOMATIC IDENTIFICATION SYSTEM (AIS) BERBASIS MINI COMPUTER PADA KAPAL NELAYAN TRADISIONAL DI MADURA

2019 ◽  
Vol 9 (2) ◽  
pp. 344
Author(s):  
Akh Maulidi
Keyword(s):  
General Purpose ◽  
Automatic Identification ◽  
Identification System ◽  
Input Output ◽  
Automatic Identification System

Automatic Identification System (AIS) merupakan sistem navigasi yang digunakan pada Vessel Traffic Services (VTS) untuk mengidentifikasi dan bertukar data secara elektronik. Organisasi Maritim Internasional (IMO) dan Konvensi Internasional untuk Keselamatan Jiwa di Laut (SOLAS) 1974 dan Colreg (collision regulation 1972) mewajibkan AIS untuk dipasang dikapal yang mempunyai gross tonnage (GT) 300 ton atau lebih dan juga untuk semua jenis kapal penumpang.AIS dapat menggantikan beberapa alat komunikasi lain yang selama ini digunakan seperti kompas, peta, radar maupun GPS. Akan tetapi fungsi dan kepraktisan yang dimiliki sebanding dengan harganya yang sangat mahal sehingga nelayan tradisional tidak dapat menjangkau harganya yang relative mahal. Dari hal tersebut, peneliti mencari alternatif lain yang dapat menjadi solusi untuk mendapatkan system navigasi yang murah dan handal.Pada Mini PC terdapat pin General Purpose Input Output (GPIO) yang dapat difungsikan sebagai penerima atau pemancar sistem navigasi. Selain itu mini PC memiliki harga yang sangat murah dibandingkan dengan perangkat sejenis. Dengan memberikan algoritma program pada mini PC dapat difungsikan sebagai AIS Pemancar (Tranceiver) untuk mengirimkan data ordinat, kecepatan, dan arah pergerakan kapal. Serta sebagai penerima (Receiver) untuk menerima data ordinat, kecepatan, dan arah pergerakan kapal. Dengan demikian AIS Mini PC dapat menjadi solusi alternatif dan membantu kapal nelayan tradisional di Madura dalam memperoleh sistem navigasi.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

scholarly journals IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model

2020 ◽  
Author(s):  
Iqbal H. Sarker ◽  
Yoosef B. Abushark ◽  
Fawaz Alsolami ◽  
Asif Irshad Khan
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Security Model ◽  
K Nearest Neighbor ◽  
Detection Model

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

The Study of the Ontology and Context Verification Based Intrusion Detection Model

2014 ◽  
Vol 644-650 ◽  
pp. 3338-3341 ◽  
Author(s):  
Guang Feng Guo
Keyword(s):  
Intrusion Detection ◽  
Knowledge Base ◽  
False Positive ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
False Positives ◽  
The Real ◽  
Detection Model ◽  
Positive Rate

During the 30-year development of the Intrusion Detection System, the problems such as the high false-positive rate have always plagued the users. Therefore, the ontology and context verification based intrusion detection model (OCVIDM) was put forward to connect the description of attack’s signatures and context effectively. The OCVIDM established the knowledge base of the intrusion detection ontology that was regarded as the center of efficient filtering platform of the false alerts to realize the automatic validation of the alarm and self-acting judgment of the real attacks, so as to achieve the goal of filtering the non-relevant positives alerts and reduce false positives.

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