scholarly journals HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway

2020 ◽  
Vol 14 (3) ◽  
pp. 231
Author(s):  
Firnanda Al Islama Achyunda Putra ◽  
Fitri Utaminingrum ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Feature Extraction ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Knn Classifier ◽  
Knn Classification ◽  
Testing Data ◽  
Main Components

Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.

scholarly journals Vision-Based Lane Line Detection System

2021 ◽  
Vol 09 (06) ◽  
Author(s):  
Poorna Vishwanth ◽  
Keyword(s):  
Automobile Industry ◽  
Detection System ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Line Detection ◽  
Vertical Edge ◽  
Horizontal Edge ◽  
Driver Assistance

Since the 1990s, the rising key issue of the automobile industry is self-driving or driverless vehicles. Apparently, one of the most important challenges for smart self-driving cars comprises lane-detecting and lane-tracking capability to ensure safety and also decrease vehicle accidents for driver assistance systems. Since road lane detection is one of the most challenging tasks, driverless vehicles must learn to observe the road from a visual perspective in order to achieve automatic driving. Most of the research Works done so far can only detect the lanes or vehicles separately. However, in this paper, we propose a method to combine lane information and vehicle/obstacle information that can support the driver assistance system, driver warning system or the lane change assistant system so that it enhances the quality of results. For the lane changing system, the system detects or tracks the lane lines and detects the vehicles on all sides of a test vehicle. In lane detection, line detection algorithms such as the Canny Edge detection algorithm are used to detect the lane edges. Kalman filter will be used to track the vehicle detected from the vehicle detection algorithm. For vehicle detection, we use Otsu’s thresholding, horizontal edge filtering and vertical edge. The vertical edge filter and the Otsu’s thresholding are used to detect the vehicles on all sides of the test vehicles, then the horizontal edge is used to verify the vehicles detected.

scholarly journals Internet-Based Flood Detection System (Iot) and Telegram Messenger Using Mcu Node and Water Level Sensor

2020 ◽  
Vol 4 (1) ◽  
pp. 230-235
Author(s):  
Novianda Nanda Nanda ◽  
Rizalul Akram ◽  
Liza Fitria
Keyword(s):  
Water Level ◽  
Early Warning ◽  
High Intensity ◽  
Rainy Season ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Level Sensor ◽  
Flood Detection ◽  
Internet Of Thing

During the rainy season, several regions in Indonesia experienced floods even to the capital of Indonesia also flooded. Some of the causes are the high intensity of continuous rain, clogged or non-smooth drainage, high tides to accommodate the flow of water from rivers, other causes such as forest destruction, shallow and full of garbage and other causes. Every flood disaster comes, often harming the residents who experience it. The late anticipation from the community and the absence of an early warning system or information that indicates that there will be a flood so that the community is not prepared to face floods that cause a lot of losses. Therefore it is necessary to have a detection system to provide early warning if floods will occur, this is very important to prevent material losses from flooded residents. From this problem the researchers designed an internet-based flood detection System of Things (IoT). This tool can later be controlled via a smartphone remotely and can send messages Telegram messenger to citizens if the detector detects a flood will occur.Keywords: Flooding, Smartphone, Telegram messenger, Internet of Thing (IoT).

A Novel Method for Tramcar Anti-Collision Warning System Based on Monocular Vision

2012 ◽  
Vol 430-432 ◽  
pp. 1871-1876
Author(s):  
Hui Bo Bi ◽  
Xiao Dong Xian ◽  
Li Juan Huang
Keyword(s):  
Software Design ◽  
Early Warning System ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Monocular Vision ◽  
System Construction ◽  
Collision Warning ◽  
Lane Width ◽  
Novel Method

For the problem of tramcar collision accident in coal mine underground, a monocular vision-based tramcar anti-collision warning system based on ARM and FPGA was designed and implemented. In this paper, we present an improved fast lane detection algorithm based on Hough transform. Besides, a new distance measurement and early-warning system based on the invariance of the lane width is proposed. System construction, hardware architecture and software design are given in detail. The experiment results show that the precision and speed of the system can satisfy the application requirement.

Stereo Vision-Based Road Debris Detection System for Advanced Driver Assistance Systems

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Naveen Kumar Bangalore Ramaiah ◽  
◽  
Subrata Kumar Kundu ◽  
Keyword(s):  
Stereo Vision ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Warning System ◽  
Detection Algorithm ◽  
Potential Threat ◽  
Safe Driving ◽  
Collision Warning ◽  
On The Road ◽  
3D Information

Reliable detection of obstacles around an autonomous vehicle is essential to avoid potential collision and ensure safe driving. However, a vast majority of existing systems are mainly focused on detecting large obstacles such as vehicles, pedestrians, and so on. Detection of small obstacles such as road debris, which pose a serious potential threat are often overlooked. In this article, a novel stereo vision-based road debris detection algorithm is proposed that detects debris on the road surfaces and estimates their height accurately. Moreover, a collision warning system that could warn the driver of an imminent crash by using 3D information of detected debris has been studied. A novel feature-based classifier that uses a combination of strong and weak features has been developed for the proposed algorithm, which identifies debris from selected candidates and calculates its height. 3D information of detected debris and vehicle’s speed are used in the collision warning system to warn the driver to safely maneuver the vehicle. The performance of the proposed algorithm has been evaluated by implementing it on a passenger vehicle. Experimental results confirm that the proposed algorithm can successfully detect debris of ≥5 cm height for up to a 22 m distance with an accuracy of 90%. Moreover, the debris detection algorithm runs at 20 Hz in a commercially available stereo camera making it suitable for real-time applications in commercial vehicles.

Implementation of Vehicle Detection System Using WSN

2013 ◽  
Vol 411-414 ◽  
pp. 1459-1464
Author(s):  
Yun Long Li ◽  
Chun Xin Wang ◽  
Xiao Li Zhou ◽  
Huan Juan Wang ◽  
Ya Kun Liu
Keyword(s):  
Road Traffic ◽  
Detection System ◽  
Vehicle Detection ◽  
Intelligent Transportation ◽  
Detection Algorithm ◽  
State Machine ◽  
Sensor Nodes ◽  
Guidance System ◽  
The Road ◽  
On The Road

Vehicle Detection System plays a basic role in the field of intelligent transportation, and is the cornerstone of constructing modern intelligent transportation system. This paper presents a new vehicle detection algorithm using WSN that called the adaptive state machine. The algorithm can adaptively update the threshold and baseline; use the state machine to achieve the aim of the accurate and efficient vehicle detection. It can be used for the detection of road traffic flow, and can be used in large parking vehicle guidance system. On the road, we have deployed 76 Sensor Nodes to evaluate the performance. We observe the accurate of the road vehicle detection rate of vehicle detection system is nearly 98%.

Realtime On-Road Vehicle Detection Approach Based on Cascaded Structure

2011 ◽  
Vol 130-134 ◽  
pp. 2429-2432
Author(s):  
Liang Xiu Zhang ◽  
Xu Yun Qiu ◽  
Zhu Lin Zhang ◽  
Yu Lin Wang
Keyword(s):  
Warning System ◽  
Vehicle Detection ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Driver Assistance ◽  
Road Vehicle ◽  
Collision Warning ◽  
Detection Approach ◽  
Transportation Applications

Realtime on-road vehicle detection is a key technology in many transportation applications, such as driver assistance, autonomous driving and active safety. A vehicle detection algorithm based on cascaded structure is introduced. Haar-like features are used to built model in this application, and GAB algorithm is chosen to train the strong classifiers. Then, the real-time on-road vehicle classifier based on cascaded structure is constructed by combining the strong classifiers. Experimental results show that the cascaded classifier is excellent in both detection accuracy and computational efficiency, which ensures its application to collision warning system.

scholarly journals EARLY WARNING TERHADAP INSIDEN KEBAKARAN PADA OVER LAND BELT CONVEYOR PABRIK SEMEN MENGGUNAKAN LINEAR HEAT DETECTION SYSTEM

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Ratna Ikawati ◽  
Widy Wibisono ◽  
Mulia F. Nasution ◽  
Ade Y Prasetya
Keyword(s):  
Optical Fiber ◽  
Early Warning ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Control Room ◽  
Belt Conveyor ◽  
Linear Heat ◽  
Early Warning Mechanism ◽  
Roller Temperature

The cement market is increasingly competitive with the rise of imported cement, which of course must be answered by cement producers among competition. One of the main processes that should not be disturbed is the supply of limestone from quarry to the factory. The safety of people, processes and equipment are certainly necessity for the production process to run smoothly. The problem that can occur in limestone supply is a breakdown on the Overland Belt Conveyor (OLBC) which is caused by a broken roller and causes the belt to tear and potentially fire. For this reason, prevention efforts need to be done with an early warning mechanism to provide alerts to interested parties. Early warning system can be applied to the process because it can detect the beginning of heat so that fires can be prevented. Therefore, it is necessary to install a sensor using optical fiber called a linear heat detector (LHD) on OLBC. Installing the LHD system on OLBC can effectively detect an increase in roller temperature which then becomes an early warning for operators in the control room 24 hours a day to prevent incidents that cause harm to the company due to belt fires or torn belts.

scholarly journals Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers

2022 ◽  
Vol 12 (2) ◽  
pp. 1565
Author(s):  
Bagus Haryadi ◽  
Po-Hao Chang ◽  
Akrom Akrom ◽  
Arifan Q. Raharjo ◽  
Galih Prakoso
Keyword(s):  
Cardiovascular Disease ◽  
Early Warning System ◽  
Blood Circulation ◽  
Detection System ◽  
Warning System ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Disease Group ◽  
Poincaré Plot ◽  
Exact Test

<span>An analysis of blood circulation was used to identify variations of heart rate and to create an early warning system of autonomic dysfunction. The Poincaré plot analyzed blood circulation using photoplethysmography (PPG) signals between non-smokers and smokers in three different indices: SD1, SD2, and SD1 SD2 ratio (SSR). There were twenty subjects separated into non-smoker and smoker groups with sample sizes of 10, respectively. An independent sample t-test to compare the continuous variables. Whereas, the comparison between two groups employed Fisher’s exact test for categorical variables. The result showed that SD1 was found to be considerably lower in the group of smokers (0.03±0.01) than that of the non-smokers (0.06±0.03). Similarly, SSR was recorded at 0.0012±0.0005 and 0.0023±0.0012 for smoking and non-smoking subjects, respectively. As a comparison, SD2 for non-smokers (25.7±0.5) was lower than smokers (27.3±0.4). In conclusion, we revealed that the parameters of Poincaré plots (SD1, SD2, and SSR) exert good performances to significantly differentiate the PPG signals of the group of non-smokers from those of smokers. We also supposed that the method promises to be a suitable method to distinguish the cardiovascular disease group. Therefore, this method can be applied as a part of early detection system of cardiovascular diseases.</span>

scholarly journals Automotive Tire Monitoring and Warning System Based on ZigBee Wireless Network

2012 ◽  
Vol 4 ◽  
pp. 27-31 ◽  
Author(s):  
Cheng Shi Luo
Keyword(s):  
Service Life ◽  
Low Power ◽  
Wireless Network ◽  
Detection System ◽  
Warning System ◽  
Tire Pressure ◽  
Ultra Low Power ◽  
Zigbee Network ◽  
Main Components ◽  
Network Device

This paper describes an automotive tire monitoring and warning system based on ZigBee wireless network. By using some components with ultra-low power like ZigBee network device, SP12 Sensor of Infineon which is made for TPMS application, MSP430F149 of TI Company, etc., the problem of inadequate service life of automotive tire pressure detection system is successfully solved. And the keyless door, automatic windows, automatic wipers, etc. of automobile are organized into an intelligent wireless network by applying ZigBee network. This paper describes the main components and provides the hardware diagram.

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