Based on the Human Eye Fatigue Detection System of Recognition and FPGA Technology

2014 ◽  
Vol 672-674 ◽  
pp. 1995-1998 ◽  
Author(s):  
Jun Wang ◽  
Qiang Liu ◽  
Hang Zhao
Keyword(s):  
High Speed ◽  
Detection System ◽  
A Priori ◽  
Color Space ◽  
Gaussian Model ◽  
Region Growing ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Threshold Detection ◽  
Human Eye

This paper introduces the field programmable gate array (fpga) application in high-speed visual inspection system.ALTERA EP1K30QC208-2 are used in the system for system calculation and control of the core, to perform high-speed real-time visual detection algorithm, this paper adopts a yawning based on eye closure and to detect driver fatigue, the method of in YCr, Cb in the color space using gaussian model skin detection of human face area, in the face of a gray binarization figure using a priori knowledge of facial features geometry in rough positioning the human eye, eye contour are obtained by region growing and morphological operation and calculation of the closure of the eye;Best threshold detection lips when using lip color roughly locate the lips, on the basis of accurate positioning lips by face grey value characteristics, and then through the mouth level to determine whether a driver yawn;Based on the two characteristics of driving fatigue, experiments show that this system detection speed, excellent versatility, and can improve the detection accuracy.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

scholarly journals Sistem Penegakan Speed Bump Berdasarkan Kecepatan Kendaraan yang Diklasifikasikan Haar Cascade Classifier

2019 ◽  
Vol 7 (1) ◽  
pp. 12-18
Author(s):  
Muhammad Zulfikri ◽  
Erni Yudaningtyas ◽  
Rahmadwati Rahmadwati
Keyword(s):  
High Speed ◽  
Road Traffic ◽  
Detection System ◽  
Warning System ◽  
Detection Accuracy ◽  
Cascade Classifier ◽  
Car Detection ◽  
Speed Measurement ◽  
Haar Cascade ◽  
Safety Limit

Driving at high speed is among the frequent causes of accidents. In this research, a warning system was developed to warn drivers when their speed beyond the safety limit. Haar cascade classifier was proposed for the detection system which comprises Haar features, integral image, AdaBoost learning, and cascade classifier. The system was implemented using Python OpenCV library and evaluated on road traffic video collected in one way traffic. As a result, the proposed method yields 97.92% of car detection accuracy in daylight and MSE of 2.88 in speed measurement.

An Enhanced Approach for Number Plate Detection and Recognition

2020 ◽  
Vol 13 ◽  
Author(s):  
Diksha Kurchaniya ◽  
Mohd. Aquib Ansari ◽  
Durga Patel
Keyword(s):  
Character Recognition ◽  
High Speed ◽  
Detection System ◽  
Wiener Filter ◽  
Noise Removal ◽  
Motor Vehicles ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Wide Range ◽  
High Speed Vehicle

Introduction: The number of vehicles is increasing day by day in our life. The vehicle may violate traffic rules and cause accidents. The automatic number plate detection system (ANPR) plays a significant role to identify these vehicles. Number plate detection is very difficult sometimes because each country has its own format for representing the number plate and font types and sizes may also vary for different vehicles. The number of ANPR systems is available nowadays but still, it is a big problem to detect the number plate correctly in various scenarios like high-speed vehicle, number plate language, etc. Methods: In the development of this method, we mainly used wiener filter for noise removal, morphological operations for number plate localization, connected component algorithm for character segmentation, and template based matching for character recognition. Results: Our proposed methodology is providing promising results in terms of detection accuracy. Discussion: The automatic number plate detection system (ANPR) has wide range of applications because the license number is the crucial, commonly putative and essential identifier of motor vehicles. These applications include ticketless parking fee management, parking access automation, car theft prevention, security guide assistance, Motorway Road Tolling, Border Control, Journey Time Measurement, Law Enforcement and many more. Conclusion: In this paper, an enhanced approach of automatic number plate detection system is proposed using some different techniques which not only detect the number plate of the vehicle but also recognize each character present in the detected number plate image.

Intrusion Detection Systems Alerts Reduction

2018 ◽  
pp. 255-275 ◽  
Author(s):  
Aymen Akremi ◽  
Hassen Sallay ◽  
Mohsen Rouached
Keyword(s):  
Intrusion Detection ◽  
High Speed ◽  
Detection System ◽  
Search Space ◽  
Relevant Information ◽  
Intrusion Detection Systems ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Rules Set ◽  
Intrusion Alerts

Investigators search usually for any kind of events related directly to an investigation case to both limit the search space and propose new hypotheses about the suspect. Intrusion detection system (IDS) provide relevant information to the forensics experts since it detects the attacks and gathers automatically several pertinent features of the network in the attack moment. Thus, IDS should be very effective in term of detection accuracy of new unknown attacks signatures, and without generating huge number of false alerts in high speed networks. This tradeoff between keeping high detection accuracy without generating false alerts is today a big challenge. As an effort to deal with false alerts generation, the authors propose new intrusion alert classifier, named Alert Miner (AM), to classify efficiently in near real-time the intrusion alerts in HSN. AM uses an outlier detection technique based on an adaptive deduced association rules set to classify the alerts automatically and without human assistance.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

scholarly journals An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1358
Author(s):  
Yan Liu ◽  
Jingwen Wang ◽  
Tiantian Qiu ◽  
Wenting Qi
Keyword(s):  
High Speed ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Input Image ◽  
Research Field ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Intelligent Traffic System ◽  
Blurred Image ◽  
Blurred Images

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

scholarly journals Mil-Std 1553 Cable Modeling for Fault Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1648-1655
Keyword(s):  
Fault Detection ◽  
Public Transportation ◽  
High Speed ◽  
Power Transmission ◽  
Detection System ◽  
Short Circuit ◽  
Detection Algorithm ◽  
Open Circuit ◽  
Test Signal ◽  
Prototype Development

Amongst major industries, the aircraft industry has gained momentum not only in public transportation, but also in defence, business and space sectors. The electrical, mechanical and electronic systems of an aircraft are all interconnected by different types of cables like hook up wires, cables for high speed data transmission, cables for power transmission, fire resistant cables, co-axial cables etc , with each type of cable having its own specifications. Military Standard 1553 (Mil-Std 1553) is one such cable primarily used for on-board aircraft sub-system communication and monitoring. Mil-Std 1553 protocol defines the physical and electrical properties of the cable. Mil-Std 1553 is a dual redundant bus, that is, there are two channels for a single bus communication. Mil-Std 1553 is prone to faults like opens or shorts because of its continuous wear and tear in aircraft environment. If a faulty cable is operated, then it possesses a high risk to the aircraft system .As of now ,there is no automatic fault detection system employed on Mil-Std 1553. Hence there is a need for automatic fault detection system on Mil-Std 1553 cables before the entire system collapses. In this regard, modeling of Mil-Std 1553 is very important since the developed model can be used for testing of the fault detection algorithm and further prototype development. Here, the Mil-Std 1553 cable has been modeled using SIMULINK/MATLAB. The cable is modeled under two different scenarios: considering only the Test Signal , considering both Test Signal and Data Signal. The cable is modeled considering all its electrical characteristics for three conditions, namely, No Fault condition, Open circuit condition and Short circuit condition. PI section is used as an elemental block for modeling of Mil-Std 1553.

scholarly journals Low-Cost Multisensor Integrated System for Online Walking Gait Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingyun Yan ◽  
Guowu Wei ◽  
Zheqi Hu ◽  
Haohua Xiu ◽  
Yuyang Wei ◽  
...  
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Integrated System ◽  
Heel Strike ◽  
Detection Accuracy ◽  
Time Error ◽  
Kinematic Data ◽  
Compact Size

A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass-produced low-cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real-time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule-based calibration filter to classify kinematic data into five distinct gait events: heel-strike, foot-flat, heel-off, toe-off, and initial-swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low-cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.

Mask Wearing Detection Algorithm Based on Improved Tiny YOLOv3

2021 ◽  
pp. 2155007
Author(s):  
Guohua Liu ◽  
Qintao Zhang
Keyword(s):  
Loss Function ◽  
Network Structure ◽  
High Speed ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Bounding Box

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on [Formula: see text]-means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability.

Detection of Color Indicators under Complex Circumstances

2012 ◽  
Vol 433-440 ◽  
pp. 6157-6161
Author(s):  
Lu Ping Fang ◽  
Yuan Jie Wei ◽  
Fei Lu
Keyword(s):  
Color Space ◽  
Region Growing ◽  
Detection Algorithm ◽  
Bayesian Decision ◽  
Vector Method ◽  
Video Frames ◽  
Error Ratio ◽  
Lab Color Space ◽  
Background Image ◽  
Color Indicator

A color indicator detection algorithm under different illumination conditions is proposed. First, based on the similarity between consecutive video frames in channel L of Lab color space, background image can be determined. Differentiation of a frame and the background can identify the motion region, and thus the search area for the color indicator is greatly reduced. Second, the convex hull of motion region is specified and sampling is taken within it. By assigning the weight, seeds can be determined using clustering method. Finally, region growing is implemented by applying Bayesian decision with minimal error ratio. The method is applicable to more different conditions and produces better results compared with traditional color-threshold vector method.

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