scholarly journals Automatic Detection of Violent Incidents from Video Footage of CCTV Cameras

2020 ◽  
pp. 464-472
Author(s):  
Baswaraju Swathi ◽  
B L Deepika Chowdary ◽  
K Sai Sindhu ◽  
Ashika P
Keyword(s):  
Anomaly Detection ◽  
State Of The Art ◽  
Traditional Approach ◽  
University Campus ◽  
Major Drawback ◽  
Public Places ◽  
Human Errors ◽  
Video Footage ◽  
Public Garden ◽  
Detection And Recognition

In the current era, the majority of public places such as supermarket, public garden, malls, university campus, etc. are under video surveillance. There is a need to provide essential security and monitor unusual anomaly activities at such places. The major drawback in the traditional approach, that there is a need to perform manual operation for 24 ? 7 and also there are possibilities of human errors. This paper focuses on anomaly detection and activity recognition of humans in the videos. Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. We present an e?cient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

scholarly journals Multiresolution dendritic cell algorithm for network anomaly detection

2021 ◽  
Vol 7 ◽  
pp. e749
Author(s):  
David Limon-Cantu ◽  
Vicente Alarcon-Aquino
Keyword(s):  
Information Systems ◽  
Dendritic Cell ◽  
Anomaly Detection ◽  
State Of The Art ◽  
Denial Of Service ◽  
Series Data ◽  
Time Frequency ◽  
Computer Services ◽  
Dendritic Cell Algorithm ◽  
Network Anomaly Detection

Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as Dendritic Cell Algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.

scholarly journals Graph Laplacian for image anomaly detection

2020 ◽  
Vol 31 (1-2) ◽  
Author(s):  
Francesco Verdoja ◽  
Marco Grangetto
Keyword(s):  
Anomaly Detection ◽  
State Of The Art ◽  
Computational Cost ◽  
Gaussian Model ◽  
Graph Laplacian ◽  
High Dimensional ◽  
Spatial Awareness ◽  
Detection Problem ◽  
Multivariate Gaussian Model ◽  
And Inversion

Abstract Reed–Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD’s limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.

scholarly journals Multisensory Nudging: A Design Intervention for Sustainable Hand-Washing Behavior in Public Space

2019 ◽  
Vol 1 (1) ◽  
pp. 3341-3350 ◽  
Author(s):  
Giulia Wally Scurati ◽  
Siyuan Huang ◽  
Siyu Wu ◽  
Tengfei Chen ◽  
Yueyao Zhang ◽  
...  
Keyword(s):  
Public Space ◽  
Water Consumption ◽  
Future Development ◽  
Conventional Treatment ◽  
Hand Washing ◽  
Test Results ◽  
University Campus ◽  
Public Places ◽  
Service Companies ◽  
Excessive Consumption

AbstractThe scarce availability of water in highly populated cities is about to become a social problem. While the water service companies work on improving the distribution network in order to reduce losses, it is evident that one of the main problems is due to an excess of use of this resource by users. This consumption is relatively controlled when excessive consumption is clearly associated, in the consumer mind, with high costs. However, when users are in public places they tend to consume water because of a loss of correlation with costs. In this paper, we describe the design of a device to be installed in public environments, which aims to reduce the consumption of water. The device measures in real time the flow of water and sends the user visual and sound information trying to create a link between consumption and costs. The device has been installed in a university campus bathroom and has been tested. Test results show a reduction in water consumption, especially in the interactive prototype approach compared to the conventional treatment. Further modifications for future development of the interactive device is also discussed.

scholarly journals Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1017 ◽  
Author(s):  
Abdulmohsen Almalawi ◽  
Adil Fahad ◽  
Zahir Tari ◽  
Asif Irshad Khan ◽  
Nouf Alzahrani ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Supervisory Control ◽  
State Of The Art ◽  
Current Approach ◽  
Industrial Processes ◽  
Parameter Choice ◽  
Detection Algorithms ◽  
Detection Approach ◽  
Unsupervised Anomaly Detection ◽  
Threshold Technique

Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms.

scholarly journals Survey of Flight Anomaly Detection Methods: Challenges and Opportunities

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Vivian Rowoli Igenewari ◽  
Zakwan Skaf ◽  
Ian K. Jennions
Keyword(s):  
Anomaly Detection ◽  
State Of The Art ◽  
Detection Methods ◽  
Aviation Industry ◽  
Contributing Factors ◽  
Current State ◽  
Challenges And Opportunities ◽  
Proactive Measures ◽  
Component Faults ◽  
Safety Enhancement

Safety enhancement is a major goal of the aviation industry owing to the predicted increase in air travel. There is also the need to prevent fatalities, increase reliability and reduce monetary costs suffered as a result of delays and accidents that still occur. Accidents today are complex as a result of many causal factors acting alone but more often as a combination with other contributing factors. In tackling this trend, proactive measures have been put in place to find hazardous combinations that occur during flights in order to mitigate them before accidents occur. Flight Anomaly Detection (AD) methods are aimed at highlighting abnormal occurrences of a flight, that are different from the norm. As an improvement on the current state-of-the-art method, previous works have proposed different AD techniques for detection of previously unknown flight risks such as component faults, aircraft operational inefficiencies and some abnormal crew behaviour. However, current AD methods individually have limitations that prevent them from detecting certain significant anomalies in flight data. This paper surveys current flight AD approaches, their strengths and limitations as well as brings to light the benefits of a hybrid AD method to extend previous work and find safety-critical events, particularly those related to abnormal crew activity: a class of events known to amount for a substantial number of accidents/incidents today. It also highlights another emerging AD application opportunity, its challenges and how AD is beneficial in addressing them.

scholarly journals Metodología para diseño de infraestructura de telecomunicaciones para campus universitarios medianos, caso La Dolorosa-UNACH. // Methodology for design of telecommunications infrastructure for medium-sized university campuses, La Dolorosa-UNACH case.

Ciencia Unemi ◽  
2017 ◽  
Vol 10 (23) ◽  
pp. 133
Author(s):  
Juan Carlos Santillán Lima ◽  
Anibal Llanga Vargas ◽  
Gustavo Chafla Altamirano
Keyword(s):  
Fiber Optics ◽  
State Of The Art ◽  
University Campus ◽  
Ict In Education ◽  
Ip Telephony ◽  
High Quality ◽  
University Campuses ◽  
Telecommunications Infrastructure ◽  
Acceptable Quality ◽  
Campus Networks

RESUMEN Se plantea una metodología para el diseño de infraestructuras de telecomunicaciones para campus universitarios medianos, aplicada en el Campus La Dolorosa de la Universidad Nacional de Chimborazo, UNACH, que garantice el acceso a los servicios en línea. Se contó con los diferentes estándares de Fibra Óptica, UTP y WIFI, publicaciones realizadas por la ITU y la IEEE, y el estándar ETSI EG 202 057-4, sobre accesos de calidad en internet, codecs de telefonía IP, artículos sobre TICS en la Educación. Dentro de esta investigación se analizó el estado del arte respecto a infraestructuras de telecomunicaciones, estudió y determinó los servicios que requieren las redes de campus universitarios y el tráfico que genera cada uno de los servicios, y por último el diseño de la infraestructura de telecomunicaciones de acuerdo a los parámetros determinados. Entre los principales resultados se evidenció que existen 1592 dispositivos que en conjunto pueden generar 6537.60Mbps en calidad alta y 100% de usuarios, y 543.28Mbps en calidad aceptable con usuarios concurrentes, y utilizando una red GPON G.987.2 se puede transmitir todo el tráfico generado. Se presenta una metodología para el diseño de infraestructuras de telecomunicaciones óptima para los requerimientos encontrados en el lugar de estudio. ABSTRACT A methodology design of a Telecommunications Infrastructures for medium-sized campus university is proposed, it is applied at La Dolorosa Campus of the “Universidad Nacional de Chimborazo” to guaranteed access to online services. Different standards of Fiber Optics, UTP and WIFI publications made by the ITU and the IEEE, and the ETSI standard EG 202 057-4, on Internet quality accesses, IP telephony codecs, articles on ICT in Education were used. This research analyzed the state of the art regarding telecommunications infrastructures, studied and determined the services required by university campus networks and the traffic generated by each of the services, as well as the design of the telecommunications infrastructure according to the determined parameters. Among the main results it is evident that there are 1592 devices that can generate 6537.60Mbps in high quality and 100% of users, and 543.28Mbps in acceptable quality with concurrent users, and using a GPON network with the standard G.987.2 can transmit all the generated traffic. A methodology is presented for the design of optimal telecommunications infrastructures for the requirements found at the study site.

scholarly journals AN APPROACH TO AUTOMATIC DETECTION AND HAZARD RISK ASSESSMENT OF LARGE PROTRUDING ROCKS IN DENSELY FORESTED HILLY REGION

2016 ◽  
Vol XLI-B3 ◽  
pp. 195-199 ◽  
Author(s):  
S. Chhatkuli ◽  
K. Kawamura ◽  
K. Manno ◽  
T. Satoh ◽  
K. Tachibana
Keyword(s):  
Fall Risk ◽  
State Of The Art ◽  
Traditional Approach ◽  
Airborne Lidar ◽  
Rock Fall ◽  
Automatic Extraction ◽  
Protection Measures ◽  
Hilly Region ◽  
Risk Zones ◽  
Hazard Risk Assessment

Rock-fall along highways or railways presents one of the major threats to transportation and human safety. So far, the only feasible way to detect the locations of such protruding rocks located in the densely forested hilly region is by physically visiting the site and assessing the situation. Highways or railways are stretched to hundreds of kilometres; hence, this traditional approach of determining rock-fall risk zones is not practical to assess the safety throughout the highways or railways. In this research, we have utilized a state-of-the-art airborne LiDAR technology and derived a workflow to automatically detect protruding rocks in densely forested hilly regions and analysed the level of hazard risks they pose. Moreover, we also performed a 3D dynamic simulation of rock-fall to envisage the event. We validated that our proposed technique could automatically detect most of the large protruding rocks in the densely forested hilly region. Automatic extraction of protruding rocks and proper risk zoning could be used to identify the most crucial place that needs the proper protection measures. Hence, the proposed technique would provide an invaluable support for the management and planning of highways and railways safety, especially in the forested hilly region.

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