scholarly journals Environment Monitoring for Anomaly Detection System Using Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3834 ◽  
Author(s):  
Van Khang Nguyen ◽  
Éric Renault ◽  
Ruben Milocco
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm ◽  
Detection System ◽  
Low Cost ◽  
Mean Shift ◽  
Detection Methods ◽  
The Road ◽  
Current Threshold ◽  
Grubbs Test ◽  
Outlier Tests

Currently, the popularity of smartphones with networking capabilities equipped with various sensors and the low cost of the Internet have opened up great opportunities for the use of smartphones for sensing systems. One of the most popular applications is the monitoring and the detection of anomalies in the environment. In this article, we propose to enhance classic road anomaly detection methods using the Grubbs test on a sliding window to make it adaptive to the local characteristics of the road. This allows more precision in the detection of potholes and also building algorithms that consume less resources on smartphones and adapt better to real conditions by applying statistical outlier tests on current threshold-based anomaly detection methods. We also include a clustering algorithm and a mean shift-based algorithm to aggregate reported anomalies on data to the server. Experiments and simulations allow us to confirm the effectiveness of the proposed methods.

scholarly journals Enabling real-time road anomaly detection via mobile edge computing

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771989131 ◽  
Author(s):  
Zengwei Zheng ◽  
Mingxuan Zhou ◽  
Yuanyi Chen ◽  
Meimei Huo ◽  
Dan Chen
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Detection System ◽  
Edge Computing ◽  
Detection Methods ◽  
Data Sets ◽  
Mobile Edge Computing ◽  
Real World Data ◽  
Subsequent Work ◽  
The Road

To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node. (2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.

scholarly journals A Low Cost Vision-Based Road-Following System for Mobile Robots

2018 ◽  
Vol 8 (9) ◽  
pp. 1635 ◽  
Author(s):  
Haojie Zhang ◽  
David Hernandez ◽  
Zhibao Su ◽  
Bo Su
Keyword(s):  
Mobile Robots ◽  
Unscented Kalman Filter ◽  
Detection System ◽  
Tracking System ◽  
Low Cost ◽  
Recognition Rate ◽  
Road Detection ◽  
The Road ◽  
Road Following ◽  
Following Error

Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.

scholarly journals A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3558 ◽  
Author(s):  
Miroslav Schneider ◽  
Zdenek Machacek ◽  
Radek Martinek ◽  
Jiri Koziorek ◽  
Rene Jaros
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Low Complexity ◽  
Recognition System ◽  
Detection Methods ◽  
Intelligent Building ◽  
Dynamic Background ◽  
Image Field ◽  
Camera Systems ◽  
Background Image

This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.

Log-Based Anomaly Detection with the Improved K-Nearest Neighbor

2020 ◽  
Vol 30 (02) ◽  
pp. 239-262 ◽  
Author(s):  
Bingming Wang ◽  
Shi Ying ◽  
Guoli Cheng ◽  
Rui Wang ◽  
Zhe Yang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Keyword Search ◽  
Mean Shift ◽  
Recall Rate ◽  
K Nearest Neighbor ◽  
Mean Shift Clustering ◽  
Negative Effect

Logs play an important role in the maintenance of large-scale systems. The number of logs which indicate normal (normal logs) differs greatly from the number of logs that indicate anomalies (abnormal logs), and the two types of logs have certain differences. To automatically obtain faults by K-Nearest Neighbor (KNN) algorithm, an outlier detection method with high accuracy, is an effective way to detect anomalies from logs. However, logs have the characteristics of large scale and very uneven samples, which will affect the results of KNN algorithm on log-based anomaly detection. Thus, we propose an improved KNN algorithm-based method which uses the existing mean-shift clustering algorithm to efficiently select the training set from massive logs. Then we assign different weights to samples with different distances, which reduces the negative effect of unbalanced distribution of the log samples on the accuracy of KNN algorithm. By comparing experiments on log sets from five supercomputers, the results show that the method we proposed can be effectively applied to log-based anomaly detection, and the accuracy, recall rate and F measure with our method are higher than those of traditional keyword search method.

scholarly journals An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francy Shu ◽  
Jeff Shu
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Nursing Facilities ◽  
Risk Zones ◽  
Set Up ◽  
Current Detection

AbstractFalls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

scholarly journals Pitot Tube-Based Icing Detection: Effect of Ice Blocking on Pressure

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xianglian Lv ◽  
Jie Guan ◽  
Shengkun Wang ◽  
Haiyang Zhang ◽  
Shijie Xue ◽  
...  
Keyword(s):  
Total Pressure ◽  
Static Pressure ◽  
Detection System ◽  
Low Cost ◽  
Pitot Tube ◽  
Detection Methods ◽  
Pressure Drops ◽  
Aerial Vehicle ◽  
Pressure Output ◽  
Detection Effect

This study aims at addressing a problem on icing detection for Unmanned Aerial Vehicle (UAV for short) because traditional icing detection methods are costly and bulky. Toward this end, a pitot-based icing detection method is proposed, and the effect of different types of icing blocking on pressure is firstly reported. An icing detection system based on the pitot tube is designed and fabricated. Icing wind tunnel results indicate that if the pitot tube is blocked by glaze ice, then the total pressure of the pitot tube decreases gradually and remains unchanged and less than static pressure. However, if the pitot tube is blocked by rime ice, then the total pressure drops to the same level as the static pressure. If the pitot tube is blocked by non-ice organic materials, then the total pressure suddenly drops to the same level as the static pressure and remains unchanged. Furthermore, if the pitot tube contacts the water droplets but does not freeze, the total pressure output value fluctuates slightly. The effect of icing on pressure is caused by differences in ice microstructure, temperature, and flow velocity. At the same time, the proposed method offers a facile and low-cost approach for UAV icing detection.

scholarly journals A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

2021 ◽  
Vol 11 (21) ◽  
pp. 10403
Author(s):  
Corbinian Nentwich ◽  
Gunther Reinhart
Keyword(s):  
Anomaly Detection ◽  
Condition Monitoring ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Health Indicators ◽  
Automated System ◽  
Trend Detection ◽  
Detection Methods ◽  
Application Data

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.

Threat Hunting in Windows Using Big Security Log Data

2020 ◽  
pp. 168-188 ◽  
Author(s):  
Mohammad Rasool Fatemi ◽  
Ali A. Ghorbani
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
Sources Of Information ◽  
Detection Systems ◽  
System Logs ◽  
Analysis System ◽  
Anomaly Detection System

System logs are one of the most important sources of information for anomaly and intrusion detection systems. In a general log-based anomaly detection system, network, devices, and host logs are all collected and used together for analysis and the detection of anomalies. However, the ever-increasing volume of logs remains as one of the main challenges that anomaly detection tools face. Based on Sysmon, this chapter proposes a host-based log analysis system that detects anomalies without using network logs to reduce the volume and to show the importance of host-based logs. The authors implement a Sysmon parser to parse and extract features from the logs and use them to perform detection methods on the data. The valuable information is successfully retained after two extensive volume reduction steps. An anomaly detection system is proposed and performed on five different datasets with up to 55,000 events which detects the attacks using the preserved logs. The analysis results demonstrate the significance of host-based logs in auditing, security monitoring, and intrusion detection systems.

scholarly journals Robust Lane-Detection Method for Low-Speed Environments

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4274 ◽  
Author(s):  
Qingquan Li ◽  
Jian Zhou ◽  
Bijun Li ◽  
Yuan Guo ◽  
Jinsheng Xiao
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Low Cost ◽  
Lane Detection ◽  
Detection Methods ◽  
Low Speed ◽  
Geometric Fitting ◽  
The Road ◽  
Edge Points ◽  
Time Requirements

Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.

scholarly journals Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 707 ◽  
Author(s):  
Yongchao Song ◽  
Yongfeng Ju ◽  
Kai Du ◽  
Weiyu Liu ◽  
Jiacheng Song
Keyword(s):  
Feature Fusion ◽  
Detection System ◽  
Geometric Mean ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Light Illumination ◽  
Road Detection ◽  
The Road

Shadows and normal light illumination and road and non-road areas are two pairs of contradictory symmetrical individuals. To achieve accurate road detection, it is necessary to remove interference caused by uneven illumination, such as shadows. This paper proposes a road detection algorithm based on a learning and illumination-independent image to solve the following problems: First, most road detection methods are sensitive to variation of illumination. Second, with traditional road detection methods based on illumination invariability, it is difficult to determine the calibration angle of the camera axis, and the sampling of road samples can be distorted. The proposed method contains three stages: The establishment of a classifier, the online capturing of an illumination-independent image, and the road detection. During the establishment of a classifier, a support vector machine (SVM) classifier for the road block is generated through training with the multi-feature fusion method. During the online capturing of an illumination-independent image, the road interest region is obtained by using a cascaded Hough transform parameterized by a parallel coordinate system. Five road blocks are obtained through the SVM classifier, and the RGB (Red, Green, Blue) space of the combined road blocks is converted to a geometric mean log chromatic space. Next, the camera axis calibration angle for each frame is determined according to the Shannon entropy so that the illumination-independent image of the respective frame is obtained. During the road detection, road sample points are extracted with the random sampling method. A confidence interval classifier of the road is established, which could separate a road from its background. This paper is based on public datasets and video sequences, which records roads of Chinese cities, suburbs, and schools in different traffic scenes. The author compares the method proposed in this paper with other sound video-based road detection methods and the results show that the method proposed in this paper can achieve a desired detection result with high quality and robustness. Meanwhile, the whole detection system can meet the real-time processing requirement.

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