scholarly journals A real-time pedestrian detection system for safety applications

2021 ◽  
Author(s):  
Rinju Alice John
Keyword(s):  
Processing Speed ◽  
Detection System ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Model Method ◽  
System A ◽  
And Performance ◽  
Performance Rate ◽  
Safety Applications

Nowadays, People are more distracted by their vulnerable devices, whenever they enter a cross road. As a result, a fatal accident or injury will occur. This motivated the need to implement a reliable pedestrian detection system. To optimize the system, a cross road scenario is considered where the driver is taking a right turn and a smart camera is used to capture consecutive pictures of the pedestrian. The consecutive frames are studied using Region Of Interest method and the Gaussian mixture model method. Once the detected pedestrian enters region of interest in less than 2 meters, a warning and automatic brake system is initiated to prevent the accident. Finally, the results of the proposed methods are compared based on the processing speed and performance rate of the Shape based detection technique (Wei Zhang, [12]). The performance rate was above 90% and processing speed was about 1 sec for the proposed methods.

scholarly journals A real-time pedestrian detection system for safety applications

2021 ◽  
Author(s):  
Rinju Alice John
Keyword(s):  
Processing Speed ◽  
Detection System ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Model Method ◽  
System A ◽  
And Performance ◽  
Performance Rate ◽  
Safety Applications

Nowadays, People are more distracted by their vulnerable devices, whenever they enter a cross road. As a result, a fatal accident or injury will occur. This motivated the need to implement a reliable pedestrian detection system. To optimize the system, a cross road scenario is considered where the driver is taking a right turn and a smart camera is used to capture consecutive pictures of the pedestrian. The consecutive frames are studied using Region Of Interest method and the Gaussian mixture model method. Once the detected pedestrian enters region of interest in less than 2 meters, a warning and automatic brake system is initiated to prevent the accident. Finally, the results of the proposed methods are compared based on the processing speed and performance rate of the Shape based detection technique (Wei Zhang, [12]). The performance rate was above 90% and processing speed was about 1 sec for the proposed methods.

Research and Performance Analysis on Pedestrian Detection Based on Optimized Hog and SVM

2014 ◽  
Vol 511-512 ◽  
pp. 247-250 ◽  
Author(s):  
Yan Xi Zhang ◽  
Kai Ping Feng
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Intelligent Vehicles ◽  
Training Set ◽  
Sliding Windows ◽  
Intelligent Robots ◽  
Histograms Of Oriented Gradients ◽  
Overall Performance ◽  
And Performance ◽  
Linear Svm

Pedestrian detection based on images is one key technology of intelligent vehicles, and it is also widely applied in intelligent robots, intelligent surveillance. This paper mainly focuses on implementing a pedestrian detection system, which is classified by linear SVM with optimized Hog (Histograms of Oriented Gradients) as the extracted features. Then some experiments were done to find out that how the changing resolution of training set, times of bootstrapping iterations and different size and steps of the sliding windows affect the overall performance of detecting systems.

scholarly journals Development of an Automatic Image Cropping and Feature Extraction System for Real-Time Warping Monitoring in 3D Printing

2021 ◽  
Author(s):  
Jiarui Xie
Keyword(s):  
Real Time ◽  
Detection System ◽  
Region Of Interest ◽  
Cost Effective ◽  
Time Warping ◽  
Time Data ◽  
Fused Filament Fabrication ◽  
System A ◽  
Effective Manner

Fused Filament Fabrication (FFF) is an additive manufacturing technology that can produce complicated structures in a simple-to-use and cost-effective manner. Although promising, the technology is prone to defects, e.g. warping, compromising the quality of the manufactured component. To avoid the adverse effects caused by warping, this thesis utilizes deep-learning algorithms to develop a warping detection system using Convolutional Neural Networks (CNN). To create such a system, a real-time data acquisition and analysis pipeline is laid out. The system is responsible for capturing a snapshot of the print layer-bylayer and simultaneously extracting the corners of the component. The extracted region-of-interest is then passed through a CNN outputting the probability of a corner being warped. If a warp is detected, a signal is sent to pause the print, thereby creating a closed-loop monitoring system. The underlying model is tested on a real-time manufacturing environment yielding a mean accuracy of 99.21%.

Angular Resolved Electron Spectroscopy with Parallel Recording

1990 ◽  
Vol 48 (2) ◽  
pp. 370-371
Author(s):  
Huang Min ◽  
P.S. Flora ◽  
C.J. Harland ◽  
J.A. Venables
Keyword(s):  
Electron Spectroscopy ◽  
Low Voltage ◽  
Solid Angle ◽  
Detection System ◽  
Voltage Electron ◽  
Cylindrical Mirror ◽  
Inner Radius ◽  
Channel Plate ◽  
And Performance ◽  
Type Detector

A cylindrical mirror analyser (CMA) has been built with a parallel recording detection system. It is being used for angular resolved electron spectroscopy (ARES) within a SEM. The CMA has been optimised for imaging applications; the inner cylinder contains a magnetically focused and scanned, 30kV, SEM electron-optical column. The CMA has a large inner radius (50.8mm) and a large collection solid angle (Ω > 1sterad). An energy resolution (ΔE/E) of 1-2% has been achieved. The design and performance of the combination SEM/CMA instrument has been described previously and the CMA and detector system has been used for low voltage electron spectroscopy. Here we discuss the use of the CMA for ARES and present some preliminary results.The CMA has been designed for an axis-to-ring focus and uses an annular type detector. This detector consists of a channel-plate/YAG/mirror assembly which is optically coupled to either a photomultiplier for spectroscopy or a TV camera for parallel detection.

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

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