scholarly journals Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed

2021 ◽  
Vol 11 (2) ◽  
pp. 897-910
Author(s):  
K. Pavani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Traffic Density ◽  
Cascade Algorithm ◽  
Real Time Traffic ◽  
Accuracy And Precision ◽  
Haar Cascade ◽  
Better Than

Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

scholarly journals The Dynamic Control of the Light Signalling Device in Real-Time

2019 ◽  
Vol 292 ◽  
pp. 03014
Author(s):  
Jan Mrazek ◽  
Lucia Duricova Mrazkova ◽  
Martin Hromada ◽  
Jana Reznickova
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Dynamic Control ◽  
Traffic Density ◽  
Light Signaling ◽  
Dynamic Traffic ◽  
Real Time Control ◽  
Real Time Traffic ◽  
Time Control ◽  
Control Dynamic

The article is focused on the issue of interval on a light signaling device. Light signaling devices operate on different systems by means of which they are controlled. The control problem is a very static setting that does not respond to real-time traffic. Important variables for dynamic real-time control are traffic density in a selected area along with average speed. These variables are interdependent and can be based on dynamic traffic control. Dynamic traffic control ensures smoother traffic through major turns. At the same time, the number of harmful CO2 emitted from the means of transport should be reduced to the air. When used in low operation, power consumption should be reduced.

Spatiotemporal Traffic Analysis using Big Data

2020 ◽  
pp. 37-40
Author(s):  
Anandakumar H ◽  
Abishek Sailesh ◽  
Muthumeenal C ◽  
Visalakshi S ◽  
Muthumani K
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Historical Data ◽  
Learning Algorithm ◽  
Ensemble Classifier ◽  
Convergence Time ◽  
Traffic Patterns ◽  
Context Model ◽  
Real Time Traffic ◽  
Context Data

In collaborated online technique traffic prediction methods is proposed with distributed context aware random forest learning algorithm .The random forest is ensemble classifier which learns different traffic and context model form distributed traffic patterns. One major challenge in predicting traffic is how much to rely on the prediction model constructed using historical data in the real-time traffic situation, which may differ from that of the historical data due to the fact that traffic situations are numerous and changing over time. The proposed algorithm is online predictor of real-time traffic, the global prediction is achieved with less convergence time .The distributed scenarios (traffic data and context data) are collected together to improve the learning accuracy of classifier. The conducted experimental results on prediction of traffic dataset prove that the proposed algorithm significantly outperforms the existing algorithm.

scholarly journals A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

2015 ◽  
Vol 83 (1) ◽  
pp. 259-280 ◽  
Author(s):  
Javier Barrachina ◽  
Piedad Garrido ◽  
Manuel Fogue ◽  
Francisco J. Martinez ◽  
Juan-Carlos Cano ◽  
...  
Keyword(s):  
Real Time ◽  
Density Estimation ◽  
Traffic Density ◽  
Real Time Traffic ◽  
Estimation System

scholarly journals DEVELOPMENT OF WEBGIS BASED REAL TIME ROAD TRAFFIC INFORMATION SYSTEM

2018 ◽  
Vol IV-5 ◽  
pp. 37-45
Author(s):  
H. Shankar ◽  
M. Sharma ◽  
K. Oberai ◽  
S. Saran
Keyword(s):  
Information System ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Cost ◽  
Traffic Information ◽  
Traffic Density ◽  
Vehicle To Vehicle ◽  
Decision Making Processes ◽  
Inductive Loop Detector

<p><strong>Abstract.</strong> Rapid increase in road traffic density results into a serious problem of Traffic Congestion (TC) in cities. During peaks hours TC is very high and hence public search least congested path for their journeys in order to minimize ravel time and hence transportation cost. In this study, a new empirical model was developed to estimate congestion levels using real time road Traffic Parameters (TPs) such as vehicle density, speed, class and vehicle-to-vehicle (V2V) gap. These real time road TPs were collected using latest generation Inductive Loop Detector (ILD) technology. Further, a WebGIS based Road Traffic Information System (RTIS) for Dehradun city was developed for real time TD analyses and visualisation. This RTIS is very useful for public and user departments for planning and decision making processes. No other such system is available in India, which handles multiple traffic parameters simultaneously to provide solution of day-to-day problems.</p>

scholarly journals Social Distancing for Covid-19 Monitoring System

2021 ◽  
pp. 49-61
Author(s):  
Raj Kushwaha ◽  
Kismat Khatri ◽  
Yogesh Mahato
Keyword(s):  
Real Time ◽  
Safe Distance ◽  
Public Places ◽  
Cascade Algorithm ◽  
Facial Mask ◽  
Social Distancing ◽  
Video Footage ◽  
Camera Systems ◽  
Haar Cascade ◽  
Increasing Demand

The battle of corona-virus and mankind is possible to be tackled as long as we maintain the basic norm of social distancing and wearing masks amongst ourselves as it is through our droplets from the respiratory tract that the virus spreads. With the increasing demand for man-force and people requiring to go to their workplaces post lockdown, it is very necessary that we save each other from the virus. In this project, we will go through a detailed explanation of how we can use Python, AI and Deep Learning to monitor social distancing at public places and workplaces are keeping a safe distance from each other by analyzing real-time video streams from the camera and also detect facial mask monitoring using OpenCV and Python. To ensure if people are following social distancing protocols in public places and workplaces, we wanted to develop a tool that can monitor if people are keeping a safe distance from one another, wearing masks or not by processing real-time video footage from the camera. People at workplaces, factories, shops can integrate this tool into their security camera systems and can monitor whether people are keeping a safe distance from each other or not along with that we detect facial mask monitoring using Python with help of haar-cascade algorithm to see whether a person is wearing a mask or not. We are also planning to include thermal screening detection to measure the temperature of the subjects, a dashboard which will display a live report of corona cases around the world. We will also include an alert system that will send a notification to the authorities if the social distancing is not followed or if the temperature exceeds the threshold. The authorities can take suitable measures to isolate the subject and thus prevent the spread of Covid-19.

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.

scholarly journals Applying the Haar-cascade Algorithm for Detecting Safety Equipment in Safety Management Systems for Multiple Working Environments

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1079
Author(s):  
Le Tran Huu Phuc ◽  
HyeJun Jeon ◽  
Nguyen Tam Nguyen Truong ◽  
Jung Jae Hak
Keyword(s):  
Safety Management ◽  
Learning Algorithm ◽  
Warning System ◽  
Warning Signal ◽  
Working Environment ◽  
Safety Equipment ◽  
Cascade Algorithm ◽  
Working Environments ◽  
Safety Warning ◽  
Haar Cascade

There are many ways to maintain the safety of workers on a working site, such as using a human supervisor, computer supervisor, and smoke–flame detecting system. In order to create a safety warning system for the working site, the machine-learning algorithm—Haar-cascade classifier—was used to build four different classes for safety equipment recognition. Then a proposed algorithm was applied to calculate a score to determine the dangerousness of the current working environment based on the safety equipment and working environment. With this data, the system decides whether it is necessary to give a warning signal. For checking the efficiency of this project, three different situations were installed with this system. Generally, with the promising outcome, this application can be used in maintaining, supervising, and controlling the safety of a worker.

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