Low rate DDoS mitigation using real-time multi threshold traffic monitoring system

2021 ◽  
Author(s):  
M. Baskar ◽  
J. Ramkumar ◽  
C. Karthikeyan ◽  
V. Anbarasu ◽  
A. Balaji ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Traffic Monitoring ◽  
Low Rate

PC-based Intelligent Traffic Monitoring System with Real-time Analysis for Smart Cities

2022 ◽  
Author(s):  
Chi-Yat Lau ◽  
Man-Ching Yuen ◽  
Ka-Ho Yueng ◽  
Cheuk-Pan Fan ◽  
On-Yi Ko ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Time Analysis ◽  
Real Time Analysis

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

Real-Time Multiple Vehicle Counter using Background Subtraction for Traffic Monitoring System

2019 ◽  
Author(s):  
De Rosal Ignatius Moses Setiadi ◽  
Rizki Ramadhan Fratama ◽  
Nurul Diyah Ayu Partiningsih ◽  
Eko Hari Rachmawanto ◽  
Christy Atika Sari ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Background Subtraction ◽  
Traffic Monitoring

An Application of Emission Monitoring System Based on Real-time Traffic Monitoring

2013 ◽  
Vol 4 (1) ◽  
pp. 51-57 ◽  
Author(s):  
Jaesun Park ◽  
Sang Boem Lim ◽  
KiHo Hong ◽  
Mu Wook Pyeon ◽  
Jin You Lin
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Traffic Monitoring ◽  
Real Time Traffic ◽  
Emission Monitoring

Real-time Virtual-network-traffic-monitoring System with FPGA Accelerator

2021 ◽  
Vol 19 (10) ◽  
pp. 51-60
Author(s):  
Yuta Ukon ◽  
Shuhei Yoshida ◽  
Shoko Ohteru ◽  
Namiko Ikeda
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Network Traffic ◽  
Traffic Monitoring ◽  
Virtual Network ◽  
Network Traffic Monitoring

scholarly journals Development of an IoT based real-time traffic monitoring system for city governance

2020 ◽  
Vol 2 ◽  
pp. 230-245
Author(s):  
Mohammed Sarrab ◽  
Supriya Pulparambil ◽  
Medhat Awadalla
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Traffic Monitoring ◽  
Real Time Traffic ◽  
City Governance
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