Motion and Gray Based Automatic Road Segment Method MGARS in Urban Traffic Surveillance

2006 ◽  
pp. 85-94
Author(s):  
Hong Liu ◽  
Jintao Li ◽  
Yueliang Qian ◽  
Shouxun Lin ◽  
Qun Liu
Keyword(s):  
Urban Traffic ◽  
Road Segment ◽  
Traffic Surveillance

scholarly journals Modelling Signal Controlled Traffic Based on Driving Behaviors

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Yang Wang ◽  
Yanyan Chen ◽  
Ning Chen
Keyword(s):  
Cellular Automaton ◽  
Numerical Simulations ◽  
Traffic Signal ◽  
Cellular Automaton Model ◽  
Urban Traffic ◽  
Road Segment ◽  
Traffic Light ◽  
Driving Behaviors ◽  
Proposed Model ◽  
Anticipatory Behavior

In urban traffic, of particular interest the traffic breakdown which is primarily resulted from the driving behaviors is emerged to respond to the traffic signal. To investigate the influences of driving behaviors on the traffic breakdown, a cellular automaton model has been developed by incorporating a number of driving behaviors typically manifesting during the different stages when the vehicle approaching a traffic light. Numerical simulations have been performed based on a road segment consisting of three sections and each section is associated with a set of rules. The numerical simulations have demonstrated that the proposed model is capable of producing the time-delayed traffic breakdown and the dissolution of the oversaturated traffic. Furthermore, it has been evidenced that the probability of the traffic breakdown can be increased by involving the slow-to-start behavior. However, the activation of the anticipatory behavior can effectively impede the transition from undersaturated to oversaturated traffic. Finally, the contributions of the driving behaviors on the traffic breakdown have been quantitatively examined.

Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic

2015 ◽  
Vol 82 (3) ◽  
pp. 357-371 ◽  
Author(s):  
Jinhui Lan ◽  
Yaoliang Jiang ◽  
Guoliang Fan ◽  
Dongyang Yu ◽  
Qi Zhang
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Obstacle Detection ◽  
Urban Traffic ◽  
Traffic Surveillance

Urban Traffic Surveillance in Smart Cities Using Radar Images

2013 ◽  
pp. 296-305 ◽  
Author(s):  
J. Sánchez-Oro ◽  
David Fernández-López ◽  
R. Cabido ◽  
Antonio S. Montemayor ◽  
Juan José Pantrigo
Keyword(s):  
Smart Cities ◽  
Urban Traffic ◽  
Traffic Surveillance ◽  
Radar Images

Increasing of the urban traffic surveillance by automatic information device

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Moldovan Mircea ◽  
Filip Nicolae
Keyword(s):  
Road Traffic ◽  
Red Light ◽  
Green Light ◽  
Urban Traffic ◽  
Just In Time ◽  
Flow Density ◽  
Traffic Surveillance ◽  
Programmable Controller ◽  
Automatic Information ◽  
Test Version

AbstractOur work consists in the development and setting of an electronic information panel designed to monitor road traffic informational characteristics (velocity, vehicle length and flow density). This information is available using two displays installed above the monitored traffic line. Wishing a “just in time” warning for the driver, the display of the instantaneous speed was represented by the green light if it falls within the legal speed and red light if it is over the legal speed. Our aim was to develop an informing, monitoring and data storing equipment, able to record the time, the speed and the length of crossing vehicles. A test version, which consists of three main modules: the detection module, a programmable controller and an information panel, was designed and a complex hardware and software equipment was manufactured. The efficiency, reliability and stability in operation were the chosen criteria for the detection module. The programmable controller processes the data collected from detectors and displays it. In the future, the hardware platform will allow the connection to other devices (eg. GPRS modem), aiming to achieve the possibility to operate independently or integrated into a system of remote macro-monitoring (idea for further development).

scholarly journals Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 594 ◽  
Author(s):  
Fukai Zhang ◽  
Ce Li ◽  
Feng Yang
Keyword(s):  
Real Time ◽  
Sequence Data ◽  
Vehicle Detection ◽  
Urban Traffic ◽  
Single Shot ◽  
Traffic Surveillance ◽  
Accurate Localization ◽  
Input Size ◽  
Comparable Accuracy ◽  
Feature Pyramid

Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires accurate localization of relatively small vehicles in complex scenes and expects real-time detection. In this paper, we present a vehicle detection framework that improves the performance of the conventional Single Shot MultiBox Detector (SSD), which effectively detects different types of vehicles in real-time. Our approach, which proposes the use of different feature extractors for localization and classification tasks in a single network, and to enhance these two feature extractors through deconvolution (D) and pooling (P) between layers in the feature pyramid, is denoted as DP-SSD. In addition, we extend the scope of the default box by adjusting its scale so that smaller default boxes can be exploited to guide DP-SSD training. Experimental results on the UA-DETRAC and KITTI datasets demonstrate that DP-SSD can achieve efficient vehicle detection for real-world traffic surveillance data in real-time. For the UA-DETRAC test set trained with UA-DETRAC trainval set, DP-SSD with the input size of 300 × 300 achieves 75.43% mAP (mean average precision) at the speed of 50.47 FPS (frames per second), and the framework with a 512 × 512 sized input reaches 77.94% mAP at 25.12 FPS using an NVIDIA GeForce GTX 1080Ti GPU. The DP-SSD shows comparable accuracy, which is better than those of the compared state-of-the-art models, except for YOLOv3.

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